{
 "cells": [
  {
   "metadata": {
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     "end_time": "2024-09-20T13:09:35.036116Z",
     "start_time": "2024-09-20T13:09:35.031547Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import gc\n",
    "import warnings\n",
    "from pylab import mpl\n",
    "# 设置显示中文字体\n",
    "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "# 设置正常显示符号\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False\n",
    "warnings.filterwarnings('ignore')"
   ],
   "id": "86473f971e76c1d5",
   "outputs": [],
   "execution_count": 137
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T13:09:50.801607Z",
     "start_time": "2024-09-20T13:09:36.598942Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 读取数据\n",
    "test_data = pd.read_csv('test_format1.csv')\n",
    "train_data = pd.read_csv('train_format1.csv')\n",
    "user_info = pd.read_csv('user_info_format1.csv')\n",
    "user_log = pd.read_csv('user_log_format1.csv')"
   ],
   "id": "15ec530b469e2c91",
   "outputs": [],
   "execution_count": 138
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T00:46:48.050019Z",
     "start_time": "2024-09-20T00:46:48.039457Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# reduce memory\n",
    "# 这个函数通过将 DataFrame 中的数值列转换为更小的数据类型来减少内存使用，这对于处理大型数据集特别有用。在处理完这个函数后，DataFrame 会使用更少的内存，这可以加快数据处理速度并减少资源消耗\n",
    "\n",
    "# 自定义reduce_mem_usage函数优化pandas DataFrame中的数据类型减少内存使用\n",
    "# 接受两个参数：df（一个 pandas DataFrame）和 verbose（一个布尔值，表示是否打印内存使用情况）\n",
    "def reduce_mem_usage(df, verbose=True):\n",
    "    # 计算并存储 DataFrame 在优化之前的总内存使用量（以兆字节为单位）。\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "\n",
    "    # 遍历 DataFrame 的每一列。\n",
    "    for col in df.columns:\n",
    "        # 获取当前列的数据类型\n",
    "        col_type = df[col].dtypes\n",
    "        # 如果当前列的数据类型在 numerics 列表中，则继续执行优化。\n",
    "        if col_type in numerics:\n",
    "            # 获取当前列的最小值和最大值。\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            # 如果列的数据类型是整数类型，则检查其值是否适合更小的整数类型。\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                # 如果列的最小值大于 int8 的最小值且最大值小于 int8 的最大值，则将列的数据类型转换为 int8。\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                # 检查是否可以转换为 int16。\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                # 检查是否可以转换为 int32。\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                # 检查是否可以转换为 int64。\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)\n",
    "            else:\n",
    "                # 如果列的数据类型不是整数，那么它可能是浮点数类型。\n",
    "                # 如果列的最小值大于 float16 的最小值且最大值小于 float16 的最大值，则将列的数据类型转换为 float16。\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                # 检查是否可以转换为 float32。\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                # 如果都不行，则将列的数据类型转换为 float64。\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "    # 计算优化后的 DataFrame 的总内存使用量。           \n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    # 如果 verbose 为 True，则打印优化前后的内存使用情况以及减少的百分比\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    # 返回优化后的 DataFrame。\n",
    "    return df"
   ],
   "id": "72266bea7b007e60",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T00:47:00.143074Z",
     "start_time": "2024-09-20T00:46:57.326158Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 优化\n",
    "train_data = reduce_mem_usage(train_data)\n",
    "test_data = reduce_mem_usage(test_data)\n",
    "user_info = reduce_mem_usage(user_info)\n",
    "user_log = reduce_mem_usage(user_log)"
   ],
   "id": "29c57a435e802580",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage after optimization is: 1.74 MB\n",
      "Decreased by 70.8%\n",
      "Memory usage after optimization is: 3.49 MB\n",
      "Decreased by 41.7%\n",
      "Memory usage after optimization is: 3.24 MB\n",
      "Decreased by 66.7%\n",
      "Memory usage after optimization is: 890.48 MB\n",
      "Decreased by 69.6%\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T00:48:41.796900Z",
     "start_time": "2024-09-20T00:48:41.790382Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据探索\n",
    "train_data.head(5)"
   ],
   "id": "ac8edff40b3b6992",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label\n",
       "0    34176         3906      0\n",
       "1    34176          121      0\n",
       "2    34176         4356      1\n",
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   "cell_type": "code",
   "source": "user_info.head(5)",
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       "   user_id  age_range  gender\n",
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       "1   234512        5.0     0.0\n",
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   "cell_type": "code",
   "source": "user_log.head(5)",
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     "data": {
      "text/plain": [
       "   user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type\n",
       "0   328862   323294     833       2882    2660.0         829            0\n",
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T00:48:53.242976Z",
     "start_time": "2024-09-20T00:48:53.234455Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 缺失值查看\n",
    "# isna()用来检测缺失值\n",
    "train_data.isna().sum()"
   ],
   "id": "cc4667f2b8171218",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0\n",
       "merchant_id    0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T00:49:00.047791Z",
     "start_time": "2024-09-20T00:49:00.029993Z"
    }
   },
   "cell_type": "code",
   "source": "test_data.isna().sum()",
   "id": "c158e791967ff857",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id             0\n",
       "merchant_id         0\n",
       "prob           261477\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:36:12.405613Z",
     "start_time": "2024-09-19T12:36:12.388953Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 查看确实值得比例\n",
    "user_info.isna().sum()/user_info.shape[0]"
   ],
   "id": "83523cd4cc38f097",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      0.000000\n",
       "age_range    0.005227\n",
       "gender       0.015173\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:36:15.235369Z",
     "start_time": "2024-09-19T12:36:14.905731Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 查看确实值得比例\n",
    "user_log.isna().sum()/user_log.shape[0]"
   ],
   "id": "d333ddc8a49141af",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0.000000\n",
       "item_id        0.000000\n",
       "cat_id         0.000000\n",
       "seller_id      0.000000\n",
       "brand_id       0.001657\n",
       "time_stamp     0.000000\n",
       "action_type    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:36:18.253399Z",
     "start_time": "2024-09-19T12:36:18.231811Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 重复值查看\n",
    "train_data.duplicated().sum()"
   ],
   "id": "89450d1b74a53274",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:36:23.282909Z",
     "start_time": "2024-09-19T12:36:23.256885Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 重复值查看\n",
    "test_data.duplicated().sum()"
   ],
   "id": "331406fe5badc3e9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:36:26.178585Z",
     "start_time": "2024-09-19T12:36:26.123307Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 重复值查看\n",
    "user_info.duplicated().sum()"
   ],
   "id": "10d52dc9f750cb0f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:36:42.957851Z",
     "start_time": "2024-09-19T12:36:28.068238Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 重复值查看\n",
    "user_log.duplicated().sum()"
   ],
   "id": "53961e67e3be4fca",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13750198"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "可视化数据",
   "id": "a92ed66fa46028a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T00:56:12.068041Z",
     "start_time": "2024-09-20T00:56:11.471885Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# 正负样本比例分布\n",
    "label_gp = train_data.groupby('label')['user_id'].count()\n",
    "print('正负样本的数量：\\n',label_gp)\n",
    "_,axe = plt.subplots(1,2,figsize=(12,6))\n",
    "train_data.label.value_counts().plot(kind='pie',autopct='%1.1f%%',shadow=True,explode=[0,0.1],ax=axe[0])\n",
    "sns.countplot(x='label',data=train_data,ax=axe[1],)\n",
    "print(\"93.9%复购，6.1%不复购\")"
   ],
   "id": "6a6649fa0c7cadba",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正负样本的数量：\n",
      " label\n",
      "0    244912\n",
      "1     15952\n",
      "Name: user_id, dtype: int64\n",
      "93.9%复购，6.1%不复购\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "a3e7b3c058d24f14"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:02:54.266241Z",
     "start_time": "2024-09-20T01:02:54.021573Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 购买次数前5的店铺\n",
    "print('选取top5店铺\\n店铺\\t购买次数')\n",
    "print(train_data.merchant_id.value_counts().head(5))\n",
    "train_data_merchant = train_data.copy()\n",
    "train_data_merchant['TOP5'] = train_data_merchant['merchant_id'].map(lambda x: 1 if x in [4044,3828,4173,1102,4976] else 0)\n",
    "train_data_merchant = train_data_merchant[train_data_merchant['TOP5']==1]\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title('Merchant VS Label')\n",
    "ax = sns.countplot(x='merchant_id',hue='label',data=train_data_merchant)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "print(\"3828的复购率是最高的\")"
   ],
   "id": "2eb3d6433e718dd2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选取top5店铺\n",
      "店铺\t购买次数\n",
      "4044    3379\n",
      "3828    3254\n",
      "4173    2542\n",
      "1102    2483\n",
      "4976    1925\n",
      "Name: merchant_id, dtype: int64\n",
      "3828的复购率是最高的\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:08:43.217892Z",
     "start_time": "2024-09-20T01:08:42.755940Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 复购概率分布\n",
    "user_repeat_buy = [rate for rate in train_data.groupby(['user_id'])['label'].mean() if rate <= 1 and rate > 0] \n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "#直方图\n",
    "sns.distplot(user_repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "#qq图\n",
    "res = stats.probplot(user_repeat_buy, plot=plt)\n",
    "print('图一实际的复购概率分布与正态分布有一定的偏差，特别是在高概率区域有一个明显的峰值')\n",
    "print('图二大部分点都接近于红色直线，这表明大多数数据点的分布与正态分布较为一致，但在尾部有一些偏离')"
   ],
   "id": "462ae92c46e43b73",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "实际的复购概率分布与正态分布有一定的偏差，特别是在高概率区域有一个明显的峰值\n",
      "大部分点都接近于红色直线，这表明大多数数据点的分布与正态分布较为一致，但在尾部有一些偏离\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:11:44.217820Z",
     "start_time": "2024-09-20T01:11:44.142743Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# train_data 和 user_info使用左链接 根据共有的列 'user_id' 进行合并\n",
    "train_data_user_info = train_data.merge(user_info,on='user_id',how='left')"
   ],
   "id": "269aca0aded277a2",
   "outputs": [],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:11:46.305563Z",
     "start_time": "2024-09-20T01:11:45.721491Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Gender VS Label')\n",
    "ax = sns.countplot(x='gender',hue='label',data=train_data_user_info)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height() "
   ],
   "id": "cd0af3bed6fff844",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:14:33.226144Z",
     "start_time": "2024-09-20T01:14:32.915258Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 性别与复购分布\n",
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['gender'])['label'].mean()] \n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(repeat_buy, plot=plt)    "
   ],
   "id": "59980bdb63b7ffc8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:58:43.684364Z",
     "start_time": "2024-09-20T01:58:43.168978Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Age VS Label')\n",
    "ax = sns.countplot(x='age_range',hue='label',data=train_data_user_info)"
   ],
   "id": "af48ea06b8f9967d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:58:46.885695Z",
     "start_time": "2024-09-20T01:58:46.630310Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 年龄与复购分布\n",
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['age_range'])['label'].mean()] \n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(repeat_buy, fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(repeat_buy,plot=plt)"
   ],
   "id": "223bcf6fb80a6e43",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:58:55.604358Z",
     "start_time": "2024-09-20T01:58:50.920242Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练集不同时间段的复购用户\n",
    "all_data_1 = user_log.merge(train_data,on=['user_id'],how='left')"
   ],
   "id": "46bddc13391bda56",
   "outputs": [],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:58:57.601823Z",
     "start_time": "2024-09-20T01:58:55.605702Z"
    }
   },
   "cell_type": "code",
   "source": "all_data_1[all_data_1['label'].notnull()].head()",
   "id": "18e3756376a7ac63",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type  \\\n",
       "419   234512   146770    1173        693    3186.0         625            0   \n",
       "420   234512   146770    1173        693    3186.0         625            0   \n",
       "421   234512  1106076     992       3783    8164.0        1016            0   \n",
       "422   234512  1106076     992       3783    8164.0        1016            0   \n",
       "423   234512   866567    1198        693    3186.0         625            0   \n",
       "\n",
       "     merchant_id  label  \n",
       "419       3018.0    0.0  \n",
       "420       3271.0    0.0  \n",
       "421       3018.0    0.0  \n",
       "422       3271.0    0.0  \n",
       "423       3018.0    0.0  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>action_type</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>234512</td>\n",
       "      <td>146770</td>\n",
       "      <td>1173</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>234512</td>\n",
       "      <td>146770</td>\n",
       "      <td>1173</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3271.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>234512</td>\n",
       "      <td>1106076</td>\n",
       "      <td>992</td>\n",
       "      <td>3783</td>\n",
       "      <td>8164.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>234512</td>\n",
       "      <td>1106076</td>\n",
       "      <td>992</td>\n",
       "      <td>3783</td>\n",
       "      <td>8164.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>3271.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>234512</td>\n",
       "      <td>866567</td>\n",
       "      <td>1198</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:58:58.552523Z",
     "start_time": "2024-09-20T01:58:57.603348Z"
    }
   },
   "cell_type": "code",
   "source": "all_data_2=all_data_1[all_data_1['label'].notnull()]",
   "id": "da5bb6a460f34997",
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:58:59.115643Z",
     "start_time": "2024-09-20T01:58:58.553530Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2_sum=all_data_2.groupby(['time_stamp'])['label'].sum().reset_index()\n",
    "all_data_2_sum.head()"
   ],
   "id": "c428af9cf5d5537e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   time_stamp   label\n",
       "0         511   943.0\n",
       "1         512   975.0\n",
       "2         513  1221.0\n",
       "3         514  1170.0\n",
       "4         515  1260.0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>511</td>\n",
       "      <td>943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>512</td>\n",
       "      <td>975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>513</td>\n",
       "      <td>1221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>514</td>\n",
       "      <td>1170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>515</td>\n",
       "      <td>1260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:59:01.820131Z",
     "start_time": "2024-09-20T01:59:01.807620Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2_sum['time_stamp'] = all_data_2_sum['time_stamp'].astype(str)\n",
    "all_data_2_sum['label'] = all_data_2_sum['label'].astype(int)"
   ],
   "id": "f9a90ebd40640fd6",
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:59:03.865076Z",
     "start_time": "2024-09-20T01:59:03.853747Z"
    }
   },
   "cell_type": "code",
   "source": [
    "a=[]\n",
    "for i in range(len(all_data_2_sum)):\n",
    "    if len(all_data_2_sum['time_stamp'][i])==3:\n",
    "        a.append(all_data_2_sum['time_stamp'][i][0])\n",
    "    else:\n",
    "        a.append(all_data_2_sum['time_stamp'][i][0:2])"
   ],
   "id": "b507a422222eadbb",
   "outputs": [],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:59:05.864396Z",
     "start_time": "2024-09-20T01:59:05.846624Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2_sum['month']=a\n",
    "all_data_2_sum=all_data_2_sum.astype(int)"
   ],
   "id": "27fd5f3bbdb047e",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:59:08.935246Z",
     "start_time": "2024-09-20T01:59:07.783538Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "c=5\n",
    "for i in range(1,8):\n",
    "    \n",
    "    plt.subplot(3, 3, i)\n",
    "    b=all_data_2_sum[all_data_2_sum[\"month\"]==c]\n",
    "    plt.plot(b['time_stamp'], b['label'], linewidth=1, color=\"orange\", marker=\"o\",label=\"Mean value\")\n",
    "  \n",
    "    c+=1"
   ],
   "id": "5e7905d0a87fd985",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 7 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:59:11.944756Z",
     "start_time": "2024-09-20T01:59:11.811047Z"
    }
   },
   "cell_type": "code",
   "source": [
    "c=all_data_2_sum.groupby(['month'])['label'].sum().reset_index()\n",
    "plt.plot(c['month'], c['label'], linewidth=1, color=\"orange\", marker=\"o\",label=\"Mean value\")"
   ],
   "id": "9c98ddcb960877f3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2510bf745e0>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "2194689d25c1118e"
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  {
   "metadata": {
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     "end_time": "2024-09-20T01:59:15.330052Z",
     "start_time": "2024-09-20T01:59:15.156526Z"
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   "cell_type": "code",
   "source": [
    "# 特征工程\n",
    "\n",
    "# 这行代码从 test_data DataFrame 中删除名为 ‘prob’ 的列\n",
    "del test_data['prob']\n",
    "# 这两行代码分别将 train_data 和 test_data 中的 ‘target’ 列设置为 1 和 -1。这通常是为了标记训练集和测试集。\n",
    "train_data['target'] = 1\n",
    "test_data['target']=-1\n",
    "# 这行代码将 train_data 和 test_data 合并成一个新 DataFrame，命名为 all_data。这通常是为了将训练集和测试集放在一起进行后续的分析或处理。\n",
    "all_data = train_data.append(test_data)\n",
    "# 这行代码使用 merge 函数将 all_data 与 user_info DataFrame 合并，基于 ‘user_id’ 列进行匹配。‘how’ 参数设置为 ‘left’ 表示左连接，这意味着 all_data 中的 ‘user_id’ 列将保持不变，而 user_info 中的 ‘user_id’ 列将与 all_data 中的 ‘user_id’ 列进行匹配。\n",
    "all_data = all_data.merge(user_info,on=['user_id'],how='left')\n",
    "# 这行代码从内存中删除 train_data、test_data 和 user_info 这三个 DataFrame。这是为了释放内存，特别是在处理大型数据集时。\n",
    "del train_data, test_data, user_info\n",
    "# 这行代码调用 Python 的垃圾收集器（garbage collector）来回收不再使用的内存。这有助于进一步释放内存，尤其是在删除大型对象后。\n",
    "gc.collect()"
   ],
   "id": "42a1d72d348f643a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32370"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 38
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  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T13:03:34.712373Z",
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   "cell_type": "code",
   "source": "all_data.head()",
   "id": "e36cb71f894f7d4e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  \n",
       "0               109  \n",
       "1               109  \n",
       "2               109  \n",
       "3               109  \n",
       "4                20  "
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     "execution_count": 41,
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   "execution_count": 41
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   "cell_type": "code",
   "source": [
    "# 删除['age_range','gender']的缺失值\n",
    "all_data.dropna(subset=['age_range','gender'],inplace=True)\n",
    "all_data.isnull().sum()"
   ],
   "id": "91733411aa22a0fe",
   "outputs": [],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T01:59:25.801409Z",
     "start_time": "2024-09-20T01:59:25.066782Z"
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   "cell_type": "code",
   "source": [
    "# 用户店铺数  根据user_id分组,并计算每个用户对应的店铺数量，重置索引\n",
    "sell= user_log.groupby(['user_id'])['seller_id'].count().reset_index()\n",
    "all_data = all_data.merge(sell,on=['user_id'],how='inner')\n",
    "all_data.rename(columns={'seller_id':'sell_sum'},inplace=True)\n",
    "all_data.head()"
   ],
   "id": "a9e23425391f9d7d",
   "outputs": [
    {
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      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum\n",
       "0    34176         3906    0.0       1        6.0     0.0       451\n",
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       "2    34176         4356    1.0       1        6.0     0.0       451\n",
       "3    34176         2217    0.0       1        6.0     0.0       451\n",
       "4   230784         4818    0.0       1        0.0     0.0        54"
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   "source": [
    "# 定义一个函数 nunique_k，它接受三个参数：data（一个 pandas DataFrame），sigle_name（要计算唯一值的列名）和 new_name_1（新列的名称）\n",
    "def nunique_k(data,sigle_name,new_name_1):\n",
    "    # 这行代码使用 groupby 方法对 user_log DataFrame 按 ‘user_id’ 列进行分组，然后对每个组计算 sigle_name 列的唯一值数量。nunique() 函数用于计算唯一值的数量，reset_index() 方法用于将结果重置为 DataFrame 格式。\n",
    "    data1=user_log.groupby(['user_id'])[sigle_name].nunique().reset_index()\n",
    "    \n",
    "    # 这行代码使用 merge 函数将原始 DataFrame data 与 data1 合并。合并基于 ‘user_id’ 列，并使用内连接（inner join）方式。\n",
    "    data_union=data.merge(data1,on=['user_id'],how='inner')\n",
    "    \n",
    "    # 这行代码将 data_union DataFrame 中的 sigle_name 列重命名为 new_name_1。rename() 函数用于重命名列，inplace=True 参数表示更改将在原 DataFrame 上进行，而不是创建一个新的 DataFrame。\n",
    "    data_union.rename(columns={sigle_name:new_name_1},inplace=True)\n",
    "    \n",
    "    # 函数返回经过操作后的 data_union DataFrame。\n",
    "    return data_union"
   ],
   "id": "3f872b86414e0600",
   "outputs": [],
   "execution_count": 50
  },
  {
   "metadata": {
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     "start_time": "2024-09-20T02:08:46.256832Z"
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   "cell_type": "code",
   "source": [
    "# 不同店铺个数\n",
    "all_data=nunique_k(all_data,'seller_id','seller_id_unique')"
   ],
   "id": "8618bf166ca8ddca",
   "outputs": [],
   "execution_count": 51
  },
  {
   "metadata": {
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     "end_time": "2024-09-20T02:09:09.567627Z",
     "start_time": "2024-09-20T02:08:59.272757Z"
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   "cell_type": "code",
   "source": [
    "# 不同品类个数\n",
    "all_data=nunique_k(all_data,'cat_id','cat_id_unique')"
   ],
   "id": "a957b458de8953af",
   "outputs": [],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:09:21.129978Z",
     "start_time": "2024-09-20T02:09:09.568621Z"
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   },
   "cell_type": "code",
   "source": [
    "# 不同品牌个数\n",
    "all_data=nunique_k(all_data,'brand_id','brand_id_unique')"
   ],
   "id": "19c27a80820777d6",
   "outputs": [],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:09:38.228229Z",
     "start_time": "2024-09-20T02:09:21.130570Z"
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   },
   "cell_type": "code",
   "source": [
    "# 不同商品个数\n",
    "all_data=nunique_k(all_data,'item_id','item_id_unique')"
   ],
   "id": "e14611048127baf6",
   "outputs": [],
   "execution_count": 54
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:09:46.327552Z",
     "start_time": "2024-09-20T02:09:38.230234Z"
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   },
   "cell_type": "code",
   "source": [
    "# 活跃天数\n",
    "all_data=nunique_k(all_data,'time_stamp','time_stamp_unique')"
   ],
   "id": "3c0ebd43f6fe5f0d",
   "outputs": [],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:09:50.257659Z",
     "start_time": "2024-09-20T02:09:46.329065Z"
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   },
   "cell_type": "code",
   "source": [
    "# 不用行为种数\n",
    "all_data=nunique_k(all_data,'action_type','action_type_unique')"
   ],
   "id": "63e3b23df1befeb7",
   "outputs": [],
   "execution_count": 56
  },
  {
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     "end_time": "2024-09-20T02:00:35.743418Z",
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   "cell_type": "code",
   "source": "all_data.head()",
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       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
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       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  item_id_unique  \\\n",
       "0               109             45              106             256   \n",
       "1               109             45              106             256   \n",
       "2               109             45              106             256   \n",
       "3               109             45              106             256   \n",
       "4                20             17               19              31   \n",
       "\n",
       "   time_stamp_unique  action_type_unique  \n",
       "0                 47                   3  \n",
       "1                 47                   3  \n",
       "2                 47                   3  \n",
       "3                 47                   3  \n",
       "4                 16                   2  "
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       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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     "execution_count": 49,
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   "execution_count": 49
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   "cell_type": "code",
   "source": "user_log.head() ",
   "id": "bed2cfa9a4e1e646",
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    {
     "data": {
      "text/plain": [
       "   user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type\n",
       "0   328862   323294     833       2882    2660.0         829            0\n",
       "1   328862   844400    1271       2882    2660.0         829            0\n",
       "2   328862   575153    1271       2882    2660.0         829            0\n",
       "3   328862   996875    1271       2882    2660.0         829            0\n",
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     },
     "execution_count": 45,
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     "output_type": "execute_result"
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   ],
   "execution_count": 45
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  {
   "metadata": {
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     "end_time": "2024-09-20T02:23:34.240461Z",
     "start_time": "2024-09-20T02:23:33.202445Z"
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   },
   "cell_type": "code",
   "source": [
    "# 活跃天数方差\n",
    "std=user_log.groupby(['user_id'])['time_stamp'].std().reset_index()\n",
    "all_data = all_data.merge(std,on=['user_id'],how='inner')\n",
    "all_data.rename(columns={'time_stamp':'time_stamp_std'},inplace=True)\n",
    "all_data.head()\n"
   ],
   "id": "61d5d9b0dbee1d3e",
   "outputs": [
    {
     "data": {
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       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  item_id_unique  \\\n",
       "0               109             45              106             256   \n",
       "1               109             45              106             256   \n",
       "2               109             45              106             256   \n",
       "3               109             45              106             256   \n",
       "4                20             17               19              31   \n",
       "\n",
       "   time_stamp_unique  action_type_unique  seller_id_unique  cat_id_unique  \\\n",
       "0                 47                   3               109             45   \n",
       "1                 47                   3               109             45   \n",
       "2                 47                   3               109             45   \n",
       "3                 47                   3               109             45   \n",
       "4                 16                   2                20             17   \n",
       "\n",
       "   brand_id_unique  item_id_unique  time_stamp_unique  action_type_unique  \\\n",
       "0              106             256                 47                   3   \n",
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       "2              106             256                 47                   3   \n",
       "3              106             256                 47                   3   \n",
       "4               19              31                 16                   2   \n",
       "\n",
       "   time_stamp_std  \n",
       "0      206.449449  \n",
       "1      206.449449  \n",
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     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:23:37.548849Z",
     "start_time": "2024-09-20T02:23:37.540395Z"
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   },
   "cell_type": "code",
   "source": [
    "def most_love(data_1,most_name,new_name_2):\n",
    "    data2=user_log.groupby(['user_id'])[most_name].apply(lambda x: Counter(x).most_common(1)[0][0]).reset_index()\n",
    "    data_union_1=data_1.merge(data2,on=['user_id'],how='inner')\n",
    "    data_union_1.rename(columns={most_name:new_name_2},inplace=True)\n",
    "    return data_union_1"
   ],
   "id": "1baed0b34740c955",
   "outputs": [],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:23:50.968981Z",
     "start_time": "2024-09-20T02:23:39.955399Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 用户最喜欢的店铺\n",
    "all_data=most_love(all_data,'seller_id','sell_id_most')"
   ],
   "id": "7bd0aa9d41e20a9",
   "outputs": [],
   "execution_count": 59
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:24:00.783977Z",
     "start_time": "2024-09-20T02:23:50.969981Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最喜欢的类目\n",
    "all_data=most_love(all_data,'cat_id','cat_id_most')"
   ],
   "id": "f85aa418a59fff46",
   "outputs": [],
   "execution_count": 60
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:24:11.549039Z",
     "start_time": "2024-09-20T02:24:00.785709Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最喜欢的品牌\n",
    "all_data=most_love(all_data,'brand_id','brand_id_most')"
   ],
   "id": "2b9f0962d0abc127",
   "outputs": [],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:24:20.116484Z",
     "start_time": "2024-09-20T02:24:11.550041Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最常见的行为动作\n",
    "all_data=most_love(all_data,'action_type','action_type_most')"
   ],
   "id": "cc342f7e90bc56fd",
   "outputs": [],
   "execution_count": 62
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:24:20.132494Z",
     "start_time": "2024-09-20T02:24:20.117476Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "a9b03c1932a19801",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  ...  cat_id_unique  \\\n",
       "0               109             45              106  ...             45   \n",
       "1               109             45              106  ...             45   \n",
       "2               109             45              106  ...             45   \n",
       "3               109             45              106  ...             45   \n",
       "4                20             17               19  ...             17   \n",
       "\n",
       "   brand_id_unique  item_id_unique  time_stamp_unique  action_type_unique  \\\n",
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       "1              106             256                 47                   3   \n",
       "2              106             256                 47                   3   \n",
       "3              106             256                 47                   3   \n",
       "4               19              31                 16                   2   \n",
       "\n",
       "   time_stamp_std  sell_id_most  cat_id_most  brand_id_most  action_type_most  \n",
       "0      206.449449           331          662         4094.0                 0  \n",
       "1      206.449449           331          662         4094.0                 0  \n",
       "2      206.449449           331          662         4094.0                 0  \n",
       "3      206.449449           331          662         4094.0                 0  \n",
       "4      218.341897          3556          407         1236.0                 0  \n",
       "\n",
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       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "      <td>3556</td>\n",
       "      <td>407</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 63
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:24:25.299557Z",
     "start_time": "2024-09-20T02:24:25.295557Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def most_love_cnt(data_1,most_name,new_name_2):\n",
    "    data2=user_log.groupby(['user_id'])[most_name].apply(lambda x: Counter(x).most_common(1)[0][1]).reset_index()\n",
    "    data_union_1=data_1.merge(data2,on=['user_id'],how='inner')\n",
    "    data_union_1.rename(columns={most_name:new_name_2},inplace=True)\n",
    "    return data_union_1"
   ],
   "id": "239ea5992454ba87",
   "outputs": [],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:24:39.385382Z",
     "start_time": "2024-09-20T02:24:27.486439Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 用户最喜欢的店铺 行为次数\n",
    "all_data=most_love_cnt(all_data,'seller_id','seller_id_most_cnt')"
   ],
   "id": "814092964a7cd087",
   "outputs": [],
   "execution_count": 65
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:24:49.749959Z",
     "start_time": "2024-09-20T02:24:39.387463Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最喜欢的类目 行为次数\n",
    "all_data=most_love_cnt(all_data,'cat_id','cat_id_most_cnt')"
   ],
   "id": "5656a1b5475f3b63",
   "outputs": [],
   "execution_count": 66
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:25:00.593667Z",
     "start_time": "2024-09-20T02:24:49.750959Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最喜欢的品牌 行为次数\n",
    "all_data=most_love_cnt(all_data,'brand_id','brand_id_most_cnt')"
   ],
   "id": "49ff9bedeef5a309",
   "outputs": [],
   "execution_count": 67
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:25:08.823550Z",
     "start_time": "2024-09-20T02:25:00.594603Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 最常见的行为动作 行为次数\n",
    "all_data=most_love_cnt(all_data,'action_type','action_type_most_cnt')"
   ],
   "id": "44abf58f1203239d",
   "outputs": [],
   "execution_count": 68
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:25:08.838862Z",
     "start_time": "2024-09-20T02:25:08.825272Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "96bc2868a5421b14",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  ...  action_type_unique  \\\n",
       "0               109             45              106  ...                   3   \n",
       "1               109             45              106  ...                   3   \n",
       "2               109             45              106  ...                   3   \n",
       "3               109             45              106  ...                   3   \n",
       "4                20             17               19  ...                   2   \n",
       "\n",
       "   time_stamp_std  sell_id_most  cat_id_most  brand_id_most  action_type_most  \\\n",
       "0      206.449449           331          662         4094.0                 0   \n",
       "1      206.449449           331          662         4094.0                 0   \n",
       "2      206.449449           331          662         4094.0                 0   \n",
       "3      206.449449           331          662         4094.0                 0   \n",
       "4      218.341897          3556          407         1236.0                 0   \n",
       "\n",
       "   seller_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                  70               98                 70   \n",
       "1                  70               98                 70   \n",
       "2                  70               98                 70   \n",
       "3                  70               98                 70   \n",
       "4                  10                9                 10   \n",
       "\n",
       "   action_type_most_cnt  \n",
       "0                   410  \n",
       "1                   410  \n",
       "2                   410  \n",
       "3                   410  \n",
       "4                    47  \n",
       "\n",
       "[5 rows x 28 columns]"
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
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       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
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       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
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       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
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       "      <td>70</td>\n",
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       "      <td>410</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>331</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
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       "      <td>17</td>\n",
       "      <td>19</td>\n",
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       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "      <td>3556</td>\n",
       "      <td>407</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
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       "<p>5 rows × 28 columns</p>\n",
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      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 69
  },
  {
   "metadata": {
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     "end_time": "2024-09-20T02:25:08.916583Z",
     "start_time": "2024-09-20T02:25:08.839851Z"
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   },
   "cell_type": "code",
   "source": "user_id_union=list(set(all_data['user_id']))",
   "id": "a02360b3c90428de",
   "outputs": [],
   "execution_count": 70
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:25:15.758695Z",
     "start_time": "2024-09-20T02:25:15.751460Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def action_type_select(data,num,new_name):\n",
    "    d=user_log.groupby(['user_id'])['action_type'].apply(lambda x: Counter(x))\n",
    "    e=dict(d)\n",
    "    k=[]\n",
    "    for i in user_id_union:\n",
    "        try: \n",
    "            k.append(e[(i,num)])\n",
    "        except KeyError:\n",
    "            k.append(0) \n",
    "    data3=pd.DataFrame({'user_id':user_id_union,new_name:k})\n",
    "    data_union_2=data.merge(data3,on=['user_id'],how='inner')\n",
    "    return data_union_2"
   ],
   "id": "8703a4908e8878b",
   "outputs": [],
   "execution_count": 71
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:25:45.207204Z",
     "start_time": "2024-09-20T02:25:18.507329Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 点击次数\n",
    "all_data=action_type_select(all_data,0,'action_type_sum_0')"
   ],
   "id": "cc2d5ed2acc1897b",
   "outputs": [],
   "execution_count": 72
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:26:09.988052Z",
     "start_time": "2024-09-20T02:25:45.208194Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 加购次数\n",
    "all_data=action_type_select(all_data,1,'action_type_sum_1')"
   ],
   "id": "68689de3267780e0",
   "outputs": [],
   "execution_count": 73
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:26:34.467642Z",
     "start_time": "2024-09-20T02:26:09.989072Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 购买次数\n",
    "all_data=action_type_select(all_data,2,'action_type_sum_2')"
   ],
   "id": "f674b9fe4735a30d",
   "outputs": [],
   "execution_count": 74
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:26:58.890147Z",
     "start_time": "2024-09-20T02:26:34.468642Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 收藏次数\n",
    "all_data=action_type_select(all_data,3,'action_type_sum_2')"
   ],
   "id": "4c7e3c54d726de85",
   "outputs": [],
   "execution_count": 75
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:26:58.905422Z",
     "start_time": "2024-09-20T02:26:58.892139Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "3e498804dea14c45",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  brand_id_unique  ...  brand_id_most  \\\n",
       "0               109             45              106  ...         4094.0   \n",
       "1               109             45              106  ...         4094.0   \n",
       "2               109             45              106  ...         4094.0   \n",
       "3               109             45              106  ...         4094.0   \n",
       "4                20             17               19  ...         1236.0   \n",
       "\n",
       "   action_type_most  seller_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                 0                  70               98                 70   \n",
       "1                 0                  70               98                 70   \n",
       "2                 0                  70               98                 70   \n",
       "3                 0                  70               98                 70   \n",
       "4                 0                  10                9                 10   \n",
       "\n",
       "   action_type_most_cnt  action_type_sum_0  action_type_sum_1  \\\n",
       "0                   410              410.0                NaN   \n",
       "1                   410              410.0                NaN   \n",
       "2                   410              410.0                NaN   \n",
       "3                   410              410.0                NaN   \n",
       "4                    47               47.0                NaN   \n",
       "\n",
       "   action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                 34.0                  7.0  \n",
       "1                 34.0                  7.0  \n",
       "2                 34.0                  7.0  \n",
       "3                 34.0                  7.0  \n",
       "4                  7.0                  NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ],
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       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 76
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:29:33.808309Z",
     "start_time": "2024-09-20T02:29:33.651470Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train = all_data[all_data['target'] == 1].reset_index(drop = True)\n",
    "test = all_data[all_data['target'] == -1].reset_index(drop = True)\n",
    "train.drop(['target'],axis=1,inplace=True)\n",
    "test.drop(['target'],axis=1,inplace=True)\n",
    "train.fillna(0,inplace=True)\n",
    "test.drop(['label'],axis=1,inplace=True)\n",
    "test.fillna(0,inplace=True)\n",
    "test.head()"
   ],
   "id": "c1f2350b1ba249a2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0   163968         4605        0.0     0.0        81                21   \n",
       "1   360576         1581        2.0     2.0        77                37   \n",
       "2    98688         1964        6.0     0.0        56                22   \n",
       "3    98688         3645        6.0     0.0        56                22   \n",
       "4   295296         3361        2.0     1.0       176                56   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  time_stamp_unique  ...  \\\n",
       "0             21               22              34                 26  ...   \n",
       "1             27               37              65                 22  ...   \n",
       "2             18               21              25                 10  ...   \n",
       "3             18               21              25                 10  ...   \n",
       "4             32               46              85                 33  ...   \n",
       "\n",
       "   brand_id_most  action_type_most  seller_id_most_cnt  cat_id_most_cnt  \\\n",
       "0         7176.0                 0                  22               17   \n",
       "1         4066.0                 0                  10               13   \n",
       "2         3636.0                 0                  11               11   \n",
       "3         3636.0                 0                  11               11   \n",
       "4          487.0                 0                  50               59   \n",
       "\n",
       "   brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                 22                    63               63.0   \n",
       "1                 10                    71               71.0   \n",
       "2                 11                    51               51.0   \n",
       "3                 11                    51               51.0   \n",
       "4                 49                   162              162.0   \n",
       "\n",
       "   action_type_sum_1  action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                0.0                 16.0                  2.0  \n",
       "1                0.0                  6.0                  0.0  \n",
       "2                0.0                  5.0                  0.0  \n",
       "3                0.0                  5.0                  0.0  \n",
       "4                0.0                  7.0                  7.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
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       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
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       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
       "      <th>action_type_sum_2_y</th>\n",
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       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98688</td>\n",
       "      <td>3645</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>21</td>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3361</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 79
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:31:57.572322Z",
     "start_time": "2024-09-20T02:31:57.486131Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all = all_data[all_data['target']==1].reset_index(drop = True)\n",
    "all.drop(['target'],axis=1,inplace=True)\n",
    "all.fillna(0,inplace=True)\n",
    "all.head()"
   ],
   "id": "23c8d467d8d18756",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0    34176         3906    0.0        6.0     0.0       451               109   \n",
       "1    34176          121    0.0        6.0     0.0       451               109   \n",
       "2    34176         4356    1.0        6.0     0.0       451               109   \n",
       "3    34176         2217    0.0        6.0     0.0       451               109   \n",
       "4   230784         4818    0.0        0.0     0.0        54                20   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  ...  brand_id_most  \\\n",
       "0             45              106             256  ...         4094.0   \n",
       "1             45              106             256  ...         4094.0   \n",
       "2             45              106             256  ...         4094.0   \n",
       "3             45              106             256  ...         4094.0   \n",
       "4             17               19              31  ...         1236.0   \n",
       "\n",
       "   action_type_most  seller_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                 0                  70               98                 70   \n",
       "1                 0                  70               98                 70   \n",
       "2                 0                  70               98                 70   \n",
       "3                 0                  70               98                 70   \n",
       "4                 0                  10                9                 10   \n",
       "\n",
       "   action_type_most_cnt  action_type_sum_0  action_type_sum_1  \\\n",
       "0                   410              410.0                0.0   \n",
       "1                   410              410.0                0.0   \n",
       "2                   410              410.0                0.0   \n",
       "3                   410              410.0                0.0   \n",
       "4                    47               47.0                0.0   \n",
       "\n",
       "   action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                 34.0                  7.0  \n",
       "1                 34.0                  7.0  \n",
       "2                 34.0                  7.0  \n",
       "3                 34.0                  7.0  \n",
       "4                  7.0                  0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ],
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       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 80
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:32:32.901658Z",
     "start_time": "2024-09-20T02:32:31.630434Z"
    }
   },
   "cell_type": "code",
   "source": "all.to_csv('all_k.csv',header=True,index=False)",
   "id": "df79e98d6c613b9b",
   "outputs": [],
   "execution_count": 81
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:32:38.663751Z",
     "start_time": "2024-09-20T02:32:35.808560Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train.to_csv('train_all_k.csv',header=True,index=False)\n",
    "test.to_csv('test_all_k.csv',header=True,index=False)"
   ],
   "id": "c342fd1e4785bc76",
   "outputs": [],
   "execution_count": 82
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:34:42.505410Z",
     "start_time": "2024-09-20T02:34:42.148457Z"
    }
   },
   "cell_type": "code",
   "source": "pd.read_csv('train_all_k.csv').head()",
   "id": "82154d749fd0e09",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0    34176         3906    0.0        6.0     0.0       451               109   \n",
       "1    34176          121    0.0        6.0     0.0       451               109   \n",
       "2    34176         4356    1.0        6.0     0.0       451               109   \n",
       "3    34176         2217    0.0        6.0     0.0       451               109   \n",
       "4   230784         4818    0.0        0.0     0.0        54                20   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  ...  brand_id_most  \\\n",
       "0             45              106             256  ...         4094.0   \n",
       "1             45              106             256  ...         4094.0   \n",
       "2             45              106             256  ...         4094.0   \n",
       "3             45              106             256  ...         4094.0   \n",
       "4             17               19              31  ...         1236.0   \n",
       "\n",
       "   action_type_most  seller_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                 0                  70               98                 70   \n",
       "1                 0                  70               98                 70   \n",
       "2                 0                  70               98                 70   \n",
       "3                 0                  70               98                 70   \n",
       "4                 0                  10                9                 10   \n",
       "\n",
       "   action_type_most_cnt  action_type_sum_0  action_type_sum_1  \\\n",
       "0                   410              410.0                0.0   \n",
       "1                   410              410.0                0.0   \n",
       "2                   410              410.0                0.0   \n",
       "3                   410              410.0                0.0   \n",
       "4                    47               47.0                0.0   \n",
       "\n",
       "   action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                 34.0                  7.0  \n",
       "1                 34.0                  7.0  \n",
       "2                 34.0                  7.0  \n",
       "3                 34.0                  7.0  \n",
       "4                  7.0                  0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ],
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
       "      <th>action_type_sum_2_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
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       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
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       "      <td>256</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
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       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
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       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 84
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:34:45.760847Z",
     "start_time": "2024-09-20T02:34:45.387376Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 建模\n",
    "train_data=pd.read_csv('train_all_k.csv')\n",
    "train_data.head()"
   ],
   "id": "8cd6df179ecbd04c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0    34176         3906    0.0        6.0     0.0       451               109   \n",
       "1    34176          121    0.0        6.0     0.0       451               109   \n",
       "2    34176         4356    1.0        6.0     0.0       451               109   \n",
       "3    34176         2217    0.0        6.0     0.0       451               109   \n",
       "4   230784         4818    0.0        0.0     0.0        54                20   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  ...  brand_id_most  \\\n",
       "0             45              106             256  ...         4094.0   \n",
       "1             45              106             256  ...         4094.0   \n",
       "2             45              106             256  ...         4094.0   \n",
       "3             45              106             256  ...         4094.0   \n",
       "4             17               19              31  ...         1236.0   \n",
       "\n",
       "   action_type_most  seller_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                 0                  70               98                 70   \n",
       "1                 0                  70               98                 70   \n",
       "2                 0                  70               98                 70   \n",
       "3                 0                  70               98                 70   \n",
       "4                 0                  10                9                 10   \n",
       "\n",
       "   action_type_most_cnt  action_type_sum_0  action_type_sum_1  \\\n",
       "0                   410              410.0                0.0   \n",
       "1                   410              410.0                0.0   \n",
       "2                   410              410.0                0.0   \n",
       "3                   410              410.0                0.0   \n",
       "4                    47               47.0                0.0   \n",
       "\n",
       "   action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                 34.0                  7.0  \n",
       "1                 34.0                  7.0  \n",
       "2                 34.0                  7.0  \n",
       "3                 34.0                  7.0  \n",
       "4                  7.0                  0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ],
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
       "      <th>action_type_sum_2_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>34176</td>\n",
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       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
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       "      <td>45</td>\n",
       "      <td>106</td>\n",
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       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 85
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:34:52.876815Z",
     "start_time": "2024-09-20T02:34:52.511335Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_data=pd.read_csv('test_all_k.csv')\n",
    "test_data.head()"
   ],
   "id": "e58b70813716e1a6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0   163968         4605        0.0     0.0        81                21   \n",
       "1   360576         1581        2.0     2.0        77                37   \n",
       "2    98688         1964        6.0     0.0        56                22   \n",
       "3    98688         3645        6.0     0.0        56                22   \n",
       "4   295296         3361        2.0     1.0       176                56   \n",
       "\n",
       "   cat_id_unique  brand_id_unique  item_id_unique  time_stamp_unique  ...  \\\n",
       "0             21               22              34                 26  ...   \n",
       "1             27               37              65                 22  ...   \n",
       "2             18               21              25                 10  ...   \n",
       "3             18               21              25                 10  ...   \n",
       "4             32               46              85                 33  ...   \n",
       "\n",
       "   brand_id_most  action_type_most  seller_id_most_cnt  cat_id_most_cnt  \\\n",
       "0         7176.0                 0                  22               17   \n",
       "1         4066.0                 0                  10               13   \n",
       "2         3636.0                 0                  11               11   \n",
       "3         3636.0                 0                  11               11   \n",
       "4          487.0                 0                  50               59   \n",
       "\n",
       "   brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                 22                    63               63.0   \n",
       "1                 10                    71               71.0   \n",
       "2                 11                    51               51.0   \n",
       "3                 11                    51               51.0   \n",
       "4                 49                   162              162.0   \n",
       "\n",
       "   action_type_sum_1  action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                0.0                 16.0                  2.0  \n",
       "1                0.0                  6.0                  0.0  \n",
       "2                0.0                  5.0                  0.0  \n",
       "3                0.0                  5.0                  0.0  \n",
       "4                0.0                  7.0                  7.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
       "      <th>action_type_sum_2_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>163968</td>\n",
       "      <td>4605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>7176.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>360576</td>\n",
       "      <td>1581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>4066.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>71</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98688</td>\n",
       "      <td>1964</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>3636.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98688</td>\n",
       "      <td>3645</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>3636.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>295296</td>\n",
       "      <td>3361</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>176</td>\n",
       "      <td>56</td>\n",
       "      <td>32</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>487.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>59</td>\n",
       "      <td>49</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 86
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:34:55.337893Z",
     "start_time": "2024-09-20T02:34:55.322037Z"
    }
   },
   "cell_type": "code",
   "source": "train_data['label'].value_counts()",
   "id": "55e14e9f6cb782cf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    241303\n",
       "1.0     15838\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 87
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:34:57.569965Z",
     "start_time": "2024-09-20T02:34:57.565944Z"
    }
   },
   "cell_type": "code",
   "source": "train_data.shape",
   "id": "50e8dd61187ff15c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(257141, 31)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 88
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:35:15.778624Z",
     "start_time": "2024-09-20T02:35:15.761913Z"
    }
   },
   "cell_type": "code",
   "source": "test_data.shape",
   "id": "5a9c5ee47f83e44b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(257626, 30)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 89
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:36:53.554427Z",
     "start_time": "2024-09-20T02:36:53.145863Z"
    }
   },
   "cell_type": "code",
   "source": "all_data=pd.read_csv('all_k.csv')",
   "id": "86af248aef306f22",
   "outputs": [],
   "execution_count": 90
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:37:47.796604Z",
     "start_time": "2024-09-20T02:37:47.704365Z"
    }
   },
   "cell_type": "code",
   "source": [
    "label = train_data['label']\n",
    "train.fillna(0,inplace=True)   \n",
    "train_data.drop(['label'],axis=1,inplace=True)\n",
    "train_data.drop(['user_id'],axis=1,inplace=True)\n",
    "train_data.drop(['merchant_id'],axis=1,inplace=True)\n",
    "train_data.head"
   ],
   "id": "c6a24f50c07394f6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of         age_range  gender  sell_sum  seller_id_unique  cat_id_unique  \\\n",
       "0             6.0     0.0       451               109             45   \n",
       "1             6.0     0.0       451               109             45   \n",
       "2             6.0     0.0       451               109             45   \n",
       "3             6.0     0.0       451               109             45   \n",
       "4             0.0     0.0        54                20             17   \n",
       "...           ...     ...       ...               ...            ...   \n",
       "257136        4.0     1.0       117                33             25   \n",
       "257137        0.0     1.0       198                38             20   \n",
       "257138        0.0     1.0       198                38             20   \n",
       "257139        0.0     1.0       198                38             20   \n",
       "257140        4.0     2.0       194                50             29   \n",
       "\n",
       "        brand_id_unique  item_id_unique  time_stamp_unique  \\\n",
       "0                   106             256                 47   \n",
       "1                   106             256                 47   \n",
       "2                   106             256                 47   \n",
       "3                   106             256                 47   \n",
       "4                    19              31                 16   \n",
       "...                 ...             ...                ...   \n",
       "257136               32              49                 12   \n",
       "257137               36              89                  6   \n",
       "257138               36              89                  6   \n",
       "257139               36              89                  6   \n",
       "257140               49             127                 23   \n",
       "\n",
       "        action_type_unique  seller_id_unique.1  ...  brand_id_most  \\\n",
       "0                        3                 109  ...         4094.0   \n",
       "1                        3                 109  ...         4094.0   \n",
       "2                        3                 109  ...         4094.0   \n",
       "3                        3                 109  ...         4094.0   \n",
       "4                        2                  20  ...         1236.0   \n",
       "...                    ...                 ...  ...            ...   \n",
       "257136                   3                  33  ...         2276.0   \n",
       "257137                   3                  38  ...         6144.0   \n",
       "257138                   3                  38  ...         6144.0   \n",
       "257139                   3                  38  ...         6144.0   \n",
       "257140                   3                  50  ...         5696.0   \n",
       "\n",
       "        action_type_most  seller_id_most_cnt  cat_id_most_cnt  \\\n",
       "0                      0                  70               98   \n",
       "1                      0                  70               98   \n",
       "2                      0                  70               98   \n",
       "3                      0                  70               98   \n",
       "4                      0                  10                9   \n",
       "...                  ...                 ...              ...   \n",
       "257136                 0                  22               15   \n",
       "257137                 0                  28               38   \n",
       "257138                 0                  28               38   \n",
       "257139                 0                  28               38   \n",
       "257140                 0                  24               33   \n",
       "\n",
       "        brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                      70                   410              410.0   \n",
       "1                      70                   410              410.0   \n",
       "2                      70                   410              410.0   \n",
       "3                      70                   410              410.0   \n",
       "4                      10                    47               47.0   \n",
       "...                   ...                   ...                ...   \n",
       "257136                 25                   107              107.0   \n",
       "257137                 28                   162              162.0   \n",
       "257138                 28                   162              162.0   \n",
       "257139                 28                   162              162.0   \n",
       "257140                 24                   181              181.0   \n",
       "\n",
       "        action_type_sum_1  action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                     0.0                 34.0                  7.0  \n",
       "1                     0.0                 34.0                  7.0  \n",
       "2                     0.0                 34.0                  7.0  \n",
       "3                     0.0                 34.0                  7.0  \n",
       "4                     0.0                  7.0                  0.0  \n",
       "...                   ...                  ...                  ...  \n",
       "257136                0.0                  9.0                  1.0  \n",
       "257137                0.0                  5.0                 31.0  \n",
       "257138                0.0                  5.0                 31.0  \n",
       "257139                0.0                  5.0                 31.0  \n",
       "257140                0.0                  9.0                  4.0  \n",
       "\n",
       "[257141 rows x 28 columns]>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 91
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:38:31.377930Z",
     "start_time": "2024-09-20T02:38:28.319923Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from functools import partial\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "import lightgbm\n",
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn import  metrics \n",
    "from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score, confusion_matrix, log_loss,  auc, precision_recall_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ],
   "id": "2823213481276d83",
   "outputs": [],
   "execution_count": 92
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:38:48.344508Z",
     "start_time": "2024-09-20T02:38:48.328407Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def model_clf(model):\n",
    "    # 这行代码使用训练数据 X_train 和对应的标签 y_train 来训练模型\n",
    "    model.fit(X_train, y_train)\n",
    "    # predict_proba 方法返回一个数组，其中包含每个样本属于每个类别的概率。在这里，我们假设模型是一个二分类器，因此返回的是一个二维数组，每行对应一个样本，每列对应一个类别的概率。\n",
    "    y_train_pred = model.predict_proba(X_train)\n",
    "    # 这行代码提取了属于正类（通常标记为1）的概率。因为 predict_proba 返回的数组第二列是正类的概率。\n",
    "    y_train_pred_pos = y_train_pred[:,1]\n",
    "    # 与训练集预测类似，这里是对测试集 X_test 进行预测，并提取正类概率。\n",
    "    y_test_pred = model.predict_proba(X_test)\n",
    "    y_test_pred_pos = y_test_pred[:,1]\n",
    "\n",
    "    auc_train = roc_auc_score(y_train, y_train_pred_pos)#AUC评分\n",
    "    auc_test = roc_auc_score(y_test, y_test_pred_pos)\n",
    "\n",
    "    print(f\"Train AUC Score {auc_train}\")\n",
    "    print(f\"Test AUC Score {auc_test}\")\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_test,y_test_pred_pos)#绘制ROC曲线\n",
    "    return fpr,tpr"
   ],
   "id": "393f218d17a262a6",
   "outputs": [],
   "execution_count": 94
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T02:38:52.584453Z",
     "start_time": "2024-09-20T02:38:50.607860Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 逻辑回归\n",
    "# StandardScaler 是一个用于标准化特征的类，它将每个特征的均值（mean）变为0，标准差（standard deviation）变为1。\n",
    "stdScaler = StandardScaler()\n",
    "# 标准化特征\n",
    "X = stdScaler.fit_transform(train_data)\n",
    "# 分割数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label,test_size=0.2, random_state=0)\n",
    "# Split the data into a training set and a test set\n",
    "\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')\n",
    "model_clf(clf)"
   ],
   "id": "79456580122e4fd1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.584287599570086\n",
      "Test AUC Score 0.577831244743788\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 2.07039337e-05, 2.07039337e-05, ...,\n",
       "        9.99896480e-01, 9.99937888e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 0.00000000e+00, 6.39181847e-04, ...,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00]))"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 95
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T06:06:59.223489Z",
     "start_time": "2024-09-20T06:06:45.075029Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 逻辑回归调优\n",
    "# StandardScaler 是一个用于标准化特征的类，它将每个特征的均值（mean）变为0，标准差（standard deviation）变为1。\n",
    "stdScaler = StandardScaler()\n",
    "# 标准化特征\n",
    "X = stdScaler.fit_transform(train_data)\n",
    "# 分割数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label,test_size=0.2, random_state=42)\n",
    "# Split the data into a training set and a test set\n",
    "\n",
    "clf = LogisticRegression(max_iter=15, random_state=0, C=1.0)\n",
    "# 网格搜索\n",
    "lr_params = {\n",
    "    # 最大迭代次数\n",
    "    'max_iter': [5,10,15],\n",
    "    # 三个不同的随机种子值\n",
    "    'random_state': [0,2,42],\n",
    "    # 三个不同的正则化强度值\n",
    "    'C': [1.0,2.0,3.0]\n",
    "}\n",
    "lr_cv = GridSearchCV(clf, param_grid=lr_params, cv=5)\n",
    "lr_cv.fit(X_train, y_train)\n",
    "lr_model = lr_cv.best_estimator_\n",
    "print(\"逻辑回归最佳模型:\", lr_model)\n",
    "model_clf(lr_model)"
   ],
   "id": "8150b3e19893c892",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归最佳模型: LogisticRegression(max_iter=15, random_state=0)\n",
      "Train AUC Score 0.5831621781179814\n",
      "Test AUC Score 0.5810193421332507\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 4.14757056e-05, 4.14757056e-05, ...,\n",
       "        9.99854835e-01, 9.99875573e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 0.00000000e+00, 3.11720698e-04, ...,\n",
       "        9.99688279e-01, 1.00000000e+00, 1.00000000e+00]))"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 122
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T03:52:27.829611Z",
     "start_time": "2024-09-20T03:52:07.921082Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# knn 模型\n",
    "stdScaler = StandardScaler()\n",
    "X = stdScaler.fit_transform(train_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label, random_state=0)\n",
    "clf = KNeighborsClassifier(n_neighbors=5)\n",
    "model_clf(clf)"
   ],
   "id": "48b7bcb0ce0897c8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.9067297315415981\n",
      "Test AUC Score 0.5220034946629257\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 1.65510849e-05, 1.15857594e-04, 2.51576491e-03,\n",
       "        3.43435012e-02, 2.63807743e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 0.00000000e+00, 5.17196793e-04, 2.58598397e-03,\n",
       "        4.13757435e-02, 3.07473494e-01, 1.00000000e+00]))"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 116
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T03:52:07.514400Z",
     "start_time": "2024-09-20T03:49:59.366441Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 步骤1: 定义参数网格\n",
    "param_grid = {\n",
    "    'n_neighbors': [3, 5, 7, 9, 11, 13, 15]  \n",
    "}\n",
    "\n",
    "# 步骤2: 初始化网格搜索\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=KNeighborsClassifier(),\n",
    "    param_grid=param_grid,\n",
    "    cv=5,  # 交叉验证的折数\n",
    "    scoring='roc_auc',  # 评分标准，这里使用AUC\n",
    "    n_jobs=-1  # 使用所有可用的CPU核心\n",
    ")\n",
    "\n",
    "# 步骤3: 执行网格搜索\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 输出最佳参数和对应的AUC分数\n",
    "print(f\"Best parameters: {grid_search.best_params_}\")\n",
    "print(f\"Best AUC score: {grid_search.best_score_}\")\n",
    "\n",
    "# 使用最佳参数重新训练模型\n",
    "best_clf = grid_search.best_estimator_\n",
    "model_clf(best_clf)"
   ],
   "id": "8bc8f69292f5f875",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'n_neighbors': 15}\n",
      "Best AUC score: 0.5216035589786442\n",
      "Train AUC Score 0.7930148059296644\n",
      "Test AUC Score 0.5181405466342853\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 2.07378528e-05, 3.31805645e-04, 2.34337737e-03,\n",
       "        1.29196823e-02, 6.26697912e-02, 2.25959644e-01, 5.89452728e-01,\n",
       "        1.00000000e+00]),\n",
       " array([0.        , 0.        , 0.        , 0.00280549, 0.02213217,\n",
       "        0.08073566, 0.25529925, 0.61066085, 1.        ]))"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 114
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T06:20:36.732268Z",
     "start_time": "2024-09-20T06:20:15.855854Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn2pmml import sklearn2pmml\n",
    "# best_clf 是通过 grid_search.best_estimator_ 获取的最佳模型\n",
    "\n",
    "# 定义PMML文件的路径和名称\n",
    "pmml_file_path = 'tianmaofugou/model.pmml'\n",
    "\n",
    "# 保存模型为PMML格式\n",
    "sklearn2pmml(best_clf, pmml_file_path, with_repr=True)\n",
    "\n",
    "# 输出保存成功的信息\n",
    "print(f\"Model saved to {pmml_file_path}\")"
   ],
   "id": "f454123245795a20",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved to tianmaofugou/model.pmml\n"
     ]
    }
   ],
   "execution_count": 123
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T07:03:27.759991Z",
     "start_time": "2024-09-20T07:03:07.193076Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# XGBOOT\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label,test_size=0.2, random_state=42, shuffle=True)\n",
    "scaler = StandardScaler()\n",
    "scaler.fit_transform(X,label)\n",
    "xg = XGBClassifier(n_estimators=2, max_depth=2, learning_rate=1)\n",
    "xg_params = {\n",
    "    # 构建随机森林时使用的决策树的数量\n",
    "    'n_estimators': [2,3,4],\n",
    "    # 最大深度\n",
    "    'max_depth': [2,5,10],\n",
    "    # 学习率\n",
    "    'learning_rate': [1,2,3]\n",
    "}\n",
    "xg_cv = GridSearchCV(xg , param_grid=xg_params, cv=5)\n",
    "xg_cv.fit(X_train, y_train)\n",
    "xg_model = xg_cv.best_estimator_\n",
    "print(\"xgboot最优模型:\", xg_model)\n",
    "model_clf(xg_model)\n"
   ],
   "id": "e01e0f2fd09d8fad",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgboot最优模型: XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
      "              colsample_bylevel=None, colsample_bynode=None,\n",
      "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "              gamma=None, grow_policy=None, importance_type=None,\n",
      "              interaction_constraints=None, learning_rate=1, max_bin=None,\n",
      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "              max_delta_step=None, max_depth=2, max_leaves=None,\n",
      "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "              multi_strategy=None, n_estimators=2, n_jobs=None,\n",
      "              num_parallel_tree=None, random_state=None, ...)\n",
      "Train AUC Score 0.5732796250491352\n",
      "Test AUC Score 0.5693724940360573\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.03255843, 0.0596628 , 0.1298397 , 0.13726385,\n",
       "        0.19174219, 0.31940441, 0.40874308, 0.59942764, 0.63074179,\n",
       "        0.76980983, 0.79332656, 1.        ]),\n",
       " array([0.        , 0.0526808 , 0.09600998, 0.19638404, 0.20573566,\n",
       "        0.27618454, 0.42705736, 0.51496259, 0.68547382, 0.71976309,\n",
       "        0.82917706, 0.8463217 , 1.        ]))"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 131
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T07:05:04.134177Z",
     "start_time": "2024-09-20T07:05:03.108241Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 随机森林\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, label, random_state=0)\n",
    "clf = RandomForestClassifier(n_estimators=10, max_depth=3, min_samples_split=12, random_state=0)\n",
    "model_clf(clf)"
   ],
   "id": "81ceef933979d9a9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5807375566355785\n",
      "Test AUC Score 0.5714041816754768\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 1.65510849e-05, 6.62043397e-05, ...,\n",
       "        9.82224135e-01, 9.82257237e-01, 1.00000000e+00]),\n",
       " array([0.        , 0.        , 0.        , ..., 0.99043186, 0.99043186,\n",
       "        1.        ]))"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 135
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-20T04:00:39.753596Z",
     "start_time": "2024-09-20T04:00:02.339287Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 随机森林\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, label, random_state=0)\n",
    "clf = RandomForestClassifier(n_estimators=2, max_depth=3, min_samples_split=12, random_state=0)\n",
    "rf_params = {\n",
    "    # 最大深度\n",
    "    'max_depth': [3,5,10],\n",
    "    # 最小样本数\n",
    "    'min_samples_split': [2,3,4],\n",
    "    # 指定了构建随机森林时使用的决策树的数量\n",
    "    'n_estimators': [1,2,3]\n",
    "}\n",
    "rf_cv  = GridSearchCV(clf, param_grid=rf_params, cv=5)\n",
    "rf_cv.fit(X_train, y_train)\n",
    "rf_model = rf_cv.best_estimator_  \n",
    "print(\"随机森林最佳模型:\", rf_model)\n",
    "model_clf(rf_model)"
   ],
   "id": "e9173ce84ad9f9f6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林最佳模型: RandomForestClassifier(max_depth=3, n_estimators=2, random_state=0)\n",
      "Train AUC Score 0.566133743423648\n",
      "Test AUC Score 0.5592676396162233\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 1.65510849e-05, 3.31021698e-05, 4.13777123e-04,\n",
       "        1.57235307e-03, 1.63855741e-03, 3.31021698e-03, 2.05067942e-02,\n",
       "        2.35521938e-02, 2.54224664e-02, 3.18277363e-02, 3.21587580e-02,\n",
       "        3.43435012e-02, 4.15597742e-02, 4.42741522e-02, 4.44562141e-02,\n",
       "        4.45720717e-02, 5.90046178e-02, 6.07259306e-02, 6.07921349e-02,\n",
       "        1.58377332e-01, 2.97621609e-01, 3.05185455e-01, 3.46463861e-01,\n",
       "        3.48615502e-01, 3.58744766e-01, 4.04210596e-01, 4.04657475e-01,\n",
       "        5.36950297e-01, 5.55470961e-01, 5.59525977e-01, 6.53800957e-01,\n",
       "        6.73579503e-01, 7.14195866e-01, 7.79986428e-01, 8.34456049e-01,\n",
       "        9.10938612e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.17196793e-04,\n",
       "        2.84458236e-03, 2.84458236e-03, 4.65477114e-03, 3.12904060e-02,\n",
       "        3.77553659e-02, 3.87897595e-02, 5.06852858e-02, 5.09438841e-02,\n",
       "        5.48228601e-02, 6.51667960e-02, 7.16317559e-02, 7.18903543e-02,\n",
       "        7.18903543e-02, 9.07680372e-02, 9.33540212e-02, 9.36126196e-02,\n",
       "        2.05327127e-01, 3.81432635e-01, 3.87380398e-01, 4.28756142e-01,\n",
       "        4.32635118e-01, 4.39875873e-01, 4.88750970e-01, 4.89268167e-01,\n",
       "        6.20894750e-01, 6.40031032e-01, 6.43651409e-01, 7.32350659e-01,\n",
       "        7.52779933e-01, 7.86914921e-01, 8.37858805e-01, 8.80268942e-01,\n",
       "        9.36901991e-01, 1.00000000e+00]))"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 121
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:34:11.480227Z",
     "start_time": "2024-09-19T11:34:08.590663Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# ExTree模型\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, label, random_state=0)\n",
    "clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=0)\n",
    "model_clf(clf)"
   ],
   "id": "9a7866aa333225f6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.9979673944771675\n",
      "Test AUC Score 0.5337288083891256\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.01317466, 0.01320777, 0.01337328, 0.01343948,\n",
       "        0.01347258, 0.01964614, 0.01966269, 0.0198613 , 0.0198944 ,\n",
       "        0.01991096, 0.01994406, 0.01997716, 0.02259223, 0.02260878,\n",
       "        0.02265844, 0.02267499, 0.02270809, 0.02270809, 0.02275774,\n",
       "        0.02456181, 0.02681276, 0.02706102, 0.02714378, 0.02716033,\n",
       "        0.02722653, 0.02722653, 0.0426687 , 0.04268525, 0.04298317,\n",
       "        0.04304937, 0.04304937, 0.04308247, 0.04339694, 0.0434135 ,\n",
       "        0.0434466 , 0.04346315, 0.05243384, 0.05250004, 0.05298002,\n",
       "        0.0541055 , 0.05455238, 0.05465168, 0.05471789, 0.05473444,\n",
       "        0.12136911, 0.12160082, 0.12163392, 0.12267664, 0.12299111,\n",
       "        0.12299111, 0.12307387, 0.12378556, 0.12380212, 0.12388487,\n",
       "        0.12393452, 0.14942319, 0.1494563 , 0.14957215, 0.14958871,\n",
       "        0.15491815, 0.15594432, 0.15615949, 0.15620914, 0.15654016,\n",
       "        0.15657326, 0.15658981, 0.3593075 , 0.3593406 , 0.35935716,\n",
       "        0.36056539, 0.36063159, 0.36092951, 0.36094606, 0.36101226,\n",
       "        0.3623529 , 0.36236945, 0.36243566, 0.36258462, 0.36261772,\n",
       "        0.40341614, 0.40344925, 0.4035651 , 0.41167514, 0.41169169,\n",
       "        0.41331369, 0.41352886, 0.41364471, 0.41367782, 0.41369437,\n",
       "        1.        ]),\n",
       " array([0.        , 0.01991208, 0.01991208, 0.02068787, 0.02094647,\n",
       "        0.02094647, 0.02922162, 0.02922162, 0.02999741, 0.02999741,\n",
       "        0.02999741, 0.02999741, 0.02999741, 0.03413499, 0.03413499,\n",
       "        0.03413499, 0.03413499, 0.03439359, 0.03465219, 0.03491078,\n",
       "        0.03801396, 0.04111715, 0.04137574, 0.04137574, 0.04137574,\n",
       "        0.04189294, 0.04215154, 0.06102922, 0.06102922, 0.06128782,\n",
       "        0.06154642, 0.06180502, 0.06206362, 0.06258081, 0.06258081,\n",
       "        0.06283941, 0.06283941, 0.07318335, 0.07318335, 0.07370054,\n",
       "        0.07473494, 0.07551073, 0.07551073, 0.07576933, 0.07576933,\n",
       "        0.15490044, 0.15490044, 0.15490044, 0.15567623, 0.15593483,\n",
       "        0.15619343, 0.15645203, 0.15748642, 0.15748642, 0.15748642,\n",
       "        0.15748642, 0.18722524, 0.18722524, 0.18748384, 0.18748384,\n",
       "        0.19524179, 0.19627618, 0.19653478, 0.19653478, 0.19653478,\n",
       "        0.19679338, 0.19679338, 0.41505043, 0.41505043, 0.41505043,\n",
       "        0.41634342, 0.41634342, 0.41660202, 0.41660202, 0.41660202,\n",
       "        0.41686062, 0.41686062, 0.41686062, 0.41686062, 0.41686062,\n",
       "        0.46392552, 0.46392552, 0.46444272, 0.47142488, 0.47142488,\n",
       "        0.47168348, 0.47168348, 0.47168348, 0.47194207, 0.47194207,\n",
       "        1.        ]))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 88
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:34:13.803007Z",
     "start_time": "2024-09-19T11:34:11.482226Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Adaboost\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, random_state=0)\n",
    "clf = AdaBoostClassifier(n_estimators=10)\n",
    "model_clf(clf)"
   ],
   "id": "42174c801fd53917",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5836438805860764\n",
      "Test AUC Score 0.5730243325815666\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 8.27554246e-05, 3.47572783e-04, 1.70476175e-03,\n",
       "        3.02884854e-03, 3.17780831e-03, 5.14738741e-03, 8.97068803e-03,\n",
       "        1.58559394e-02, 2.07054072e-02, 2.16984723e-02, 2.21950049e-02,\n",
       "        2.48100763e-02, 2.58527947e-02, 5.88722091e-02, 6.81408166e-02,\n",
       "        6.81739188e-02, 7.18482597e-02, 7.64329102e-02, 7.75583840e-02,\n",
       "        7.82535295e-02, 8.28878333e-02, 8.64959698e-02, 8.65125209e-02,\n",
       "        8.98061868e-02, 9.55825154e-02, 1.06208312e-01, 1.98530264e-01,\n",
       "        2.02866648e-01, 2.03048710e-01, 2.06905113e-01, 2.10695311e-01,\n",
       "        2.11870438e-01, 2.12019398e-01, 2.12184909e-01, 2.30953839e-01,\n",
       "        2.45998775e-01, 2.46247041e-01, 2.49176584e-01, 2.50417915e-01,\n",
       "        2.53512968e-01, 2.59338950e-01, 2.59802380e-01, 2.82858041e-01,\n",
       "        3.02239362e-01, 3.05019944e-01, 3.05036495e-01, 3.06708155e-01,\n",
       "        3.07403300e-01, 3.11292805e-01, 3.12881709e-01, 3.12947914e-01,\n",
       "        3.13129976e-01, 3.13146527e-01, 3.26784621e-01, 3.54772505e-01,\n",
       "        3.70396730e-01, 3.70611894e-01, 3.70727751e-01, 3.74484847e-01,\n",
       "        3.83058309e-01, 3.85673381e-01, 3.85689932e-01, 3.99857661e-01,\n",
       "        4.14703984e-01, 4.14770188e-01, 4.19536901e-01, 4.19669309e-01,\n",
       "        4.19917576e-01, 4.19967229e-01, 4.28457935e-01, 4.39811980e-01,\n",
       "        4.39861633e-01, 4.40027144e-01, 4.43304259e-01, 4.45985534e-01,\n",
       "        4.47028253e-01, 4.48203380e-01, 4.50785349e-01, 4.51132922e-01,\n",
       "        4.52142538e-01, 4.52192191e-01, 4.72202453e-01, 5.19389596e-01,\n",
       "        5.24255615e-01, 5.48751221e-01, 5.75348814e-01, 5.76739105e-01,\n",
       "        5.77699068e-01, 6.13962495e-01, 6.16627220e-01, 6.16825833e-01,\n",
       "        6.16875486e-01, 6.17206508e-01, 6.17223059e-01, 6.22386997e-01,\n",
       "        6.22734570e-01, 6.25945481e-01, 6.25962032e-01, 6.26011685e-01,\n",
       "        6.26822688e-01, 6.29686026e-01, 6.29719128e-01, 6.31589401e-01,\n",
       "        6.31870769e-01, 6.31920422e-01, 6.32152138e-01, 6.42182095e-01,\n",
       "        6.51070028e-01, 6.51103130e-01, 6.75830451e-01, 6.75863553e-01,\n",
       "        6.83311541e-01, 6.84999752e-01, 6.87532068e-01, 6.87565170e-01,\n",
       "        6.87631374e-01, 6.99812973e-01, 7.56318377e-01, 7.56516990e-01,\n",
       "        7.57195584e-01, 7.57410748e-01, 7.57476953e-01, 7.73465301e-01,\n",
       "        7.73912180e-01, 7.74276304e-01, 8.70454658e-01, 8.70487760e-01,\n",
       "        8.70520863e-01, 8.70537414e-01, 8.72258727e-01, 8.72324931e-01,\n",
       "        8.72705606e-01, 8.72804912e-01, 8.72821463e-01, 8.86327149e-01,\n",
       "        9.00643837e-01, 9.01719658e-01, 9.02149986e-01, 9.02365150e-01,\n",
       "        9.03093398e-01, 9.03722339e-01, 9.03805094e-01, 9.22590576e-01,\n",
       "        9.26480081e-01, 9.29955809e-01, 9.30005462e-01, 9.30121319e-01,\n",
       "        9.32157103e-01, 9.32239858e-01, 9.32256409e-01, 9.32438471e-01,\n",
       "        9.32504676e-01, 9.32554329e-01, 9.56073421e-01, 9.56089972e-01,\n",
       "        9.56735464e-01, 9.64961353e-01, 9.67758487e-01, 9.67775038e-01,\n",
       "        9.67857793e-01, 9.68023304e-01, 9.69430146e-01, 9.70406660e-01,\n",
       "        9.72177626e-01, 9.72839670e-01, 9.73021732e-01, 9.73054834e-01,\n",
       "        9.73104487e-01, 9.73253447e-01, 9.73799633e-01, 9.73849286e-01,\n",
       "        9.74809249e-01, 9.77142952e-01, 9.77209156e-01, 9.77358116e-01,\n",
       "        9.80105596e-01, 9.80204902e-01, 9.91939622e-01, 9.91956173e-01,\n",
       "        9.92254092e-01, 9.92353399e-01, 9.92403052e-01, 9.92568563e-01,\n",
       "        9.92849931e-01, 9.92866482e-01, 9.92899585e-01, 9.94091263e-01,\n",
       "        9.94852613e-01, 9.94885715e-01, 9.94951919e-01, 9.98013870e-01,\n",
       "        9.98080074e-01, 9.98129727e-01, 9.98874526e-01, 9.98907628e-01,\n",
       "        9.99139344e-01, 9.99751734e-01, 9.99867591e-01, 9.99983449e-01,\n",
       "        1.00000000e+00]),\n",
       " array([0.00000000e+00, 2.58598397e-04, 1.03439359e-03, 4.65477114e-03,\n",
       "        6.98215671e-03, 6.98215671e-03, 1.03439359e-02, 2.01706749e-02,\n",
       "        2.94802172e-02, 3.74967675e-02, 4.03413499e-02, 4.08585467e-02,\n",
       "        4.52547194e-02, 4.68063098e-02, 9.72329972e-02, 1.14300491e-01,\n",
       "        1.14559090e-01, 1.19472459e-01, 1.25161624e-01, 1.26971813e-01,\n",
       "        1.26971813e-01, 1.37574347e-01, 1.43780709e-01, 1.43780709e-01,\n",
       "        1.46366693e-01, 1.55417636e-01, 1.68088958e-01, 2.88854409e-01,\n",
       "        2.94543574e-01, 2.95060771e-01, 3.01008534e-01, 3.05663305e-01,\n",
       "        3.06956297e-01, 3.06956297e-01, 3.07214895e-01, 3.32816137e-01,\n",
       "        3.45746056e-01, 3.46521852e-01, 3.49625032e-01, 3.50918024e-01,\n",
       "        3.53245410e-01, 3.57382984e-01, 3.57641583e-01, 3.83760021e-01,\n",
       "        4.03930696e-01, 4.05223688e-01, 4.05223688e-01, 4.06258081e-01,\n",
       "        4.06516680e-01, 4.12723041e-01, 4.16343419e-01, 4.16343419e-01,\n",
       "        4.16602017e-01, 4.16602017e-01, 4.29273339e-01, 4.56167572e-01,\n",
       "        4.73493664e-01, 4.73493664e-01, 4.73752263e-01, 4.77889837e-01,\n",
       "        4.88750970e-01, 4.90302560e-01, 4.90302560e-01, 5.01939488e-01,\n",
       "        5.22885958e-01, 5.22885958e-01, 5.25213344e-01, 5.25471942e-01,\n",
       "        5.25730540e-01, 5.25730540e-01, 5.33747091e-01, 5.46677011e-01,\n",
       "        5.46677011e-01, 5.46677011e-01, 5.50555987e-01, 5.51590380e-01,\n",
       "        5.52107577e-01, 5.53917766e-01, 5.54693561e-01, 5.55469356e-01,\n",
       "        5.56245151e-01, 5.56245151e-01, 5.74605637e-01, 6.20636152e-01,\n",
       "        6.25808120e-01, 6.48047582e-01, 6.68476855e-01, 6.68994052e-01,\n",
       "        6.68994052e-01, 7.07266615e-01, 7.09594001e-01, 7.10369796e-01,\n",
       "        7.10369796e-01, 7.10628394e-01, 7.10628394e-01, 7.15024567e-01,\n",
       "        7.15541764e-01, 7.18127748e-01, 7.18127748e-01, 7.18127748e-01,\n",
       "        7.18644944e-01, 7.23041117e-01, 7.23299716e-01, 7.24592708e-01,\n",
       "        7.24851306e-01, 7.24851306e-01, 7.25368503e-01, 7.35971037e-01,\n",
       "        7.42694595e-01, 7.42694595e-01, 7.62348073e-01, 7.62348073e-01,\n",
       "        7.68295837e-01, 7.68813033e-01, 7.70364624e-01, 7.70364624e-01,\n",
       "        7.70364624e-01, 7.80967158e-01, 8.24928885e-01, 8.25187484e-01,\n",
       "        8.25704681e-01, 8.26221877e-01, 8.26480476e-01, 8.37341608e-01,\n",
       "        8.37600207e-01, 8.37600207e-01, 9.06645979e-01, 9.06904577e-01,\n",
       "        9.06904577e-01, 9.06904577e-01, 9.08973364e-01, 9.08973364e-01,\n",
       "        9.09490561e-01, 9.09490561e-01, 9.09490561e-01, 9.17507111e-01,\n",
       "        9.30695630e-01, 9.31212826e-01, 9.31471425e-01, 9.31730023e-01,\n",
       "        9.32505818e-01, 9.33281614e-01, 9.33281614e-01, 9.48797517e-01,\n",
       "        9.50090509e-01, 9.51900698e-01, 9.51900698e-01, 9.51900698e-01,\n",
       "        9.52935092e-01, 9.52935092e-01, 9.52935092e-01, 9.53193690e-01,\n",
       "        9.53193690e-01, 9.53193690e-01, 9.70519783e-01, 9.70519783e-01,\n",
       "        9.71295578e-01, 9.77243341e-01, 9.79570727e-01, 9.79570727e-01,\n",
       "        9.79829325e-01, 9.79829325e-01, 9.80863719e-01, 9.81639514e-01,\n",
       "        9.82415309e-01, 9.82932506e-01, 9.83191104e-01, 9.83191104e-01,\n",
       "        9.83449703e-01, 9.83708301e-01, 9.84225498e-01, 9.84225498e-01,\n",
       "        9.84484096e-01, 9.85259891e-01, 9.85518490e-01, 9.85518490e-01,\n",
       "        9.87845875e-01, 9.87845875e-01, 9.95862426e-01, 9.95862426e-01,\n",
       "        9.95862426e-01, 9.95862426e-01, 9.95862426e-01, 9.96379622e-01,\n",
       "        9.96379622e-01, 9.96379622e-01, 9.96379622e-01, 9.96638221e-01,\n",
       "        9.96896819e-01, 9.96896819e-01, 9.96896819e-01, 9.98448410e-01,\n",
       "        9.98448410e-01, 9.98448410e-01, 9.99224205e-01, 9.99224205e-01,\n",
       "        9.99224205e-01, 9.99741402e-01, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00]))"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 89
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:34:15.653234Z",
     "start_time": "2024-09-19T11:34:13.804006Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# GBDT\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, random_state=0)\n",
    "clf = GradientBoostingClassifier(n_estimators=10, learning_rate=1.0, max_depth=1, random_state=0)\n",
    "model_clf(clf)"
   ],
   "id": "b101d2ef0eea8f9c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5835221995665941\n",
      "Test AUC Score 0.5766901539273581\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 6.62043397e-05, 9.93065095e-05, 6.62043397e-04,\n",
       "        9.93065095e-04, 1.10892269e-03, 1.34394810e-02, 1.36546451e-02,\n",
       "        1.36711961e-02, 1.42670352e-02, 1.57731839e-02, 1.59552459e-02,\n",
       "        1.96130356e-02, 1.99771595e-02, 2.01592214e-02, 2.06557540e-02,\n",
       "        2.23439646e-02, 3.96563995e-02, 3.97391549e-02, 4.64092421e-02,\n",
       "        5.01663384e-02, 5.25331435e-02, 5.28476142e-02, 5.28641652e-02,\n",
       "        5.39565369e-02, 5.43703140e-02, 5.47509889e-02, 5.57109519e-02,\n",
       "        5.94349460e-02, 6.26127543e-02, 1.80473030e-01, 1.82525365e-01,\n",
       "        1.87854814e-01, 1.87887916e-01, 1.88119631e-01, 1.93300121e-01,\n",
       "        1.94111124e-01, 1.94226982e-01, 2.05299657e-01, 2.05779639e-01,\n",
       "        2.10447045e-01, 2.18043993e-01, 2.18143299e-01, 2.25955411e-01,\n",
       "        2.31483474e-01, 2.32128966e-01, 2.36647412e-01, 2.41943759e-01,\n",
       "        2.42158924e-01, 2.94460352e-01, 2.97803671e-01, 2.98780185e-01,\n",
       "        2.98978798e-01, 3.13312038e-01, 3.14222347e-01, 3.19601450e-01,\n",
       "        3.22762707e-01, 3.73690395e-01, 3.75808934e-01, 3.86815406e-01,\n",
       "        3.86831957e-01, 4.09738658e-01, 4.16259786e-01, 4.16640461e-01,\n",
       "        4.33340505e-01, 4.35094920e-01, 4.66442675e-01, 4.73609295e-01,\n",
       "        4.74023072e-01, 4.75429914e-01, 4.90623810e-01, 4.90690015e-01,\n",
       "        4.99081415e-01, 5.07754183e-01, 5.09972029e-01, 5.12388487e-01,\n",
       "        5.13580165e-01, 5.58897036e-01, 6.00705076e-01, 6.03038779e-01,\n",
       "        6.03386352e-01, 6.46468826e-01, 6.50606597e-01, 6.64211589e-01,\n",
       "        7.02477697e-01, 7.04827951e-01, 7.05556199e-01, 7.07972658e-01,\n",
       "        7.07989209e-01, 7.08816763e-01, 7.09065029e-01, 7.36423973e-01,\n",
       "        7.37681855e-01, 7.37714957e-01, 7.52875751e-01, 7.54117082e-01,\n",
       "        7.59860309e-01, 7.84620732e-01, 7.84670385e-01, 7.86590311e-01,\n",
       "        8.40348235e-01, 9.32802595e-01, 9.32968106e-01, 9.47119284e-01,\n",
       "        9.47566163e-01, 9.47880634e-01, 9.55957563e-01, 9.57778182e-01,\n",
       "        9.69910128e-01, 9.70390109e-01, 9.70439762e-01, 9.72823119e-01,\n",
       "        9.75703007e-01, 9.94670551e-01, 9.95514656e-01, 9.97087009e-01,\n",
       "        9.99271752e-01, 9.99751734e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 5.17196793e-04, 1.03439359e-03, 2.84458236e-03,\n",
       "        3.87897595e-03, 4.39617274e-03, 2.63770365e-02, 2.66356349e-02,\n",
       "        2.66356349e-02, 2.76700284e-02, 3.20662012e-02, 3.23247996e-02,\n",
       "        4.03413499e-02, 4.16343419e-02, 4.18929403e-02, 4.37031290e-02,\n",
       "        4.52547194e-02, 6.98215671e-02, 6.98215671e-02, 8.43030773e-02,\n",
       "        8.92164469e-02, 9.23196276e-02, 9.36126196e-02, 9.36126196e-02,\n",
       "        9.61986036e-02, 9.61986036e-02, 9.69743988e-02, 9.93017843e-02,\n",
       "        1.03956555e-01, 1.08611327e-01, 2.61184381e-01, 2.63511766e-01,\n",
       "        2.71528317e-01, 2.71528317e-01, 2.72045513e-01, 2.78510473e-01,\n",
       "        2.80062064e-01, 2.80579260e-01, 2.92733385e-01, 2.93250582e-01,\n",
       "        3.01267132e-01, 3.08507887e-01, 3.09025084e-01, 3.19369020e-01,\n",
       "        3.26609775e-01, 3.27644169e-01, 3.34109129e-01, 3.39022498e-01,\n",
       "        3.39798293e-01, 3.99017326e-01, 4.00051720e-01, 4.01086113e-01,\n",
       "        4.01086113e-01, 4.16602017e-01, 4.18153607e-01, 4.25911559e-01,\n",
       "        4.30824929e-01, 4.86164986e-01, 4.87716576e-01, 4.95215930e-01,\n",
       "        4.95474528e-01, 5.23144557e-01, 5.28833721e-01, 5.29092320e-01,\n",
       "        5.45125420e-01, 5.46935609e-01, 5.78484613e-01, 5.83397983e-01,\n",
       "        5.83656581e-01, 5.84173778e-01, 5.97103698e-01, 5.97362296e-01,\n",
       "        6.06671839e-01, 6.13136799e-01, 6.14171192e-01, 6.16498578e-01,\n",
       "        6.17532971e-01, 6.57874321e-01, 6.92785105e-01, 6.94595294e-01,\n",
       "        6.94595294e-01, 7.37005431e-01, 7.39332816e-01, 7.50969744e-01,\n",
       "        7.86139126e-01, 7.88725110e-01, 7.90018102e-01, 7.92345487e-01,\n",
       "        7.92345487e-01, 7.92604086e-01, 7.92862684e-01, 8.16395138e-01,\n",
       "        8.17429532e-01, 8.17429532e-01, 8.29325058e-01, 8.30359452e-01,\n",
       "        8.34238428e-01, 8.56995087e-01, 8.57253685e-01, 8.57512283e-01,\n",
       "        9.00181019e-01, 9.62503232e-01, 9.62761831e-01, 9.70519783e-01,\n",
       "        9.70519783e-01, 9.70778381e-01, 9.75174554e-01, 9.76726144e-01,\n",
       "        9.83449703e-01, 9.83708301e-01, 9.83708301e-01, 9.84484096e-01,\n",
       "        9.85518490e-01, 9.97155418e-01, 9.97672614e-01, 9.99224205e-01,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00]))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 90
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:35:20.914603Z",
     "start_time": "2024-09-19T11:34:15.655320Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "\n",
    "# 假设 train_data 和 target 已经定义好\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, test_size=0.4, random_state=0)\n",
    "X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)\n",
    "\n",
    "# 创建 LightGBM 的 Dataset 对象\n",
    "train_matrix = lgb.Dataset(X_train, label=y_train)\n",
    "test_matrix = lgb.Dataset(X_test, label=y_test)\n",
    "\n",
    "params = {\n",
    "    'boosting_type': 'gbdt',\n",
    "    #'boosting_type': 'dart',\n",
    "    'objective': 'multiclass',\n",
    "    'metric': 'multi_logloss',\n",
    "    'min_child_weight': 1.5,\n",
    "    'num_leaves': 2**5,\n",
    "    'lambda_l2': 10,\n",
    "    'subsample': 0.7,\n",
    "    'colsample_bytree': 0.7,\n",
    "    'colsample_bylevel': 0.7,\n",
    "    'learning_rate': 0.03,\n",
    "    'tree_method': 'exact',\n",
    "    'seed': 2017,\n",
    "    \"num_class\": 2,\n",
    "    'silent': True,\n",
    "}\n",
    "\n",
    "num_round = 10000\n",
    "\n",
    "# 使用 train 方法进行训练，不传入 early_stopping_rounds 参数\n",
    "model = lgb.train(params, train_matrix, num_round, valid_sets=test_matrix)\n",
    "\n",
    "y_train_pred = model.predict(X_train, num_iteration=model.best_iteration)\n",
    "y_train_pred_pos = y_train_pred[:, 1]\n",
    "\n",
    "y_test_pred = model.predict(X_valid, num_iteration=model.best_iteration)\n",
    "y_test_pred_pos = y_test_pred[:, 1]\n",
    "\n",
    "auc_train = roc_auc_score(y_train, y_train_pred_pos)\n",
    "auc_test = roc_auc_score(y_valid, y_test_pred_pos)\n",
    "\n",
    "print(f\"Train AUC Score {auc_train}\")\n",
    "print(f\"Test AUC Score {auc_test}\")\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_valid, y_test_pred_pos)"
   ],
   "id": "489efec0e5c2e6ad",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Unknown parameter: colsample_bylevel\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "[LightGBM] [Warning] Unknown parameter: silent\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_bylevel\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "[LightGBM] [Warning] Unknown parameter: silent\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004499 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 3985\n",
      "[LightGBM] [Info] Number of data points in the train set: 154284, number of used features: 22\n",
      "[LightGBM] [Warning] Unknown parameter: colsample_bylevel\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "[LightGBM] [Warning] Unknown parameter: silent\n",
      "[LightGBM] [Info] Start training from score -0.064388\n",
      "[LightGBM] [Info] Start training from score -2.774847\n",
      "Train AUC Score 0.9940012450313334\n",
      "Test AUC Score 0.5534785433197821\n"
     ]
    }
   ],
   "execution_count": 91
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:38:42.325327Z",
     "start_time": "2024-09-19T11:38:42.308317Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_auc_curve(fpr, tpr, auc):\n",
    "    plt.figure(figsize = (16,6))\n",
    "    plt.plot(fpr,tpr,'b+',linestyle = '-')\n",
    "    plt.fill_between(fpr, tpr, alpha = 0.5)\n",
    "    plt.ylabel('True Postive Rate')\n",
    "    plt.xlabel('False Postive Rate')\n",
    "    plt.title(f'ROC Curve Having AUC = {auc}')"
   ],
   "id": "606b13a9f33e36a9",
   "outputs": [],
   "execution_count": 92
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:38:44.850888Z",
     "start_time": "2024-09-19T11:38:44.663656Z"
    }
   },
   "cell_type": "code",
   "source": "plot_auc_curve(fpr, tpr, auc_test)",
   "id": "567fd56e688a0880",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 93
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:19.587651Z",
     "start_time": "2024-09-19T11:38:47.247532Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import xgboost\n",
    "\n",
    "# xgb\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, target, test_size=0.4, random_state=0)\n",
    "X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)\n",
    "\n",
    "clf = xgboost\n",
    "\n",
    "train_matrix = clf.DMatrix(X_train, label=y_train, missing=-1)\n",
    "test_matrix = clf.DMatrix(X_test, label=y_test, missing=-1)\n",
    "z = clf.DMatrix(X_valid, label=y_valid, missing=-1)\n",
    "params = {'booster': 'gbtree',\n",
    "          'objective': 'multi:softprob',\n",
    "          'eval_metric': 'mlogloss',\n",
    "          'gamma': 1,\n",
    "          'min_child_weight': 1.5,\n",
    "          'max_depth': 5,\n",
    "          'lambda': 100,\n",
    "          'subsample': 0.7,\n",
    "          'colsample_bytree': 0.7,\n",
    "          'colsample_bylevel': 0.7,\n",
    "          'eta': 0.03,\n",
    "          'tree_method': 'exact',\n",
    "          'seed': 2017,\n",
    "          \"num_class\": 2\n",
    "          }\n",
    "score_dict={}\n",
    "num_round = 10000\n",
    "early_stopping_rounds = 100\n",
    "watchlist = [(train_matrix, 'train'),\n",
    "             (test_matrix, 'eval')\n",
    "             ]\n",
    "model = clf.train(params,\n",
    "                  train_matrix,\n",
    "                  num_boost_round=num_round,#训练的轮数，即生成的树的数量\n",
    "                  evals=watchlist,#训练时用于评估模型的数据集合，用户可以通过验证集来评估模型好坏\n",
    "                  early_stopping_rounds=early_stopping_rounds,\n",
    "                  #激活早停止，当验证的错误率early_stopping_rounds轮未下降，则停止训练。\n",
    "                  #如果发生了早停止，则模型会提供三个额外的字段：bst.best_score, bst.best_iteration 和 bst.best_ntree_limit\n",
    "                  evals_result =score_dict\n",
    "                  )\n",
    "y_train_pred = model.predict(train_matrix ,ntree_limit=model.best_iteration)#ntree_limit  用于预测的树的数量\n",
    "y_train_pred_pos = y_train_pred[:,1]\n",
    "\n",
    "y_test_pred = model.predict(z,ntree_limit=model.best_iteration)\n",
    "y_test_pred_pos = y_test_pred[:,1]\n",
    "\n",
    "auc_train = roc_auc_score(y_train, y_train_pred_pos)#AUC评分\n",
    "\n",
    "auc_test = roc_auc_score(y_valid, y_test_pred_pos)\n",
    "\n",
    "print(f\"Train AUC Score {auc_train}\")\n",
    "print(f\"Test AUC Score {auc_test}\")\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_valid,y_test_pred_pos)#绘制ROC曲线"
   ],
   "id": "152d95e61bd1cb54",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mlogloss:0.67057\teval-mlogloss:0.67051\n",
      "[1]\ttrain-mlogloss:0.64930\teval-mlogloss:0.64918\n",
      "[2]\ttrain-mlogloss:0.62924\teval-mlogloss:0.62907\n",
      "[3]\ttrain-mlogloss:0.61028\teval-mlogloss:0.61006\n",
      "[4]\ttrain-mlogloss:0.59236\teval-mlogloss:0.59208\n",
      "[5]\ttrain-mlogloss:0.57542\teval-mlogloss:0.57509\n",
      "[6]\ttrain-mlogloss:0.55936\teval-mlogloss:0.55898\n",
      "[7]\ttrain-mlogloss:0.54415\teval-mlogloss:0.54373\n",
      "[8]\ttrain-mlogloss:0.52972\teval-mlogloss:0.52925\n",
      "[9]\ttrain-mlogloss:0.51603\teval-mlogloss:0.51551\n",
      "[10]\ttrain-mlogloss:0.50303\teval-mlogloss:0.50247\n",
      "[11]\ttrain-mlogloss:0.49069\teval-mlogloss:0.49008\n",
      "[12]\ttrain-mlogloss:0.47890\teval-mlogloss:0.47825\n",
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      "[519]\ttrain-mlogloss:0.22306\teval-mlogloss:0.22753\n",
      "[520]\ttrain-mlogloss:0.22305\teval-mlogloss:0.22753\n",
      "[521]\ttrain-mlogloss:0.22304\teval-mlogloss:0.22753\n",
      "[522]\ttrain-mlogloss:0.22303\teval-mlogloss:0.22753\n",
      "[523]\ttrain-mlogloss:0.22302\teval-mlogloss:0.22753\n",
      "[524]\ttrain-mlogloss:0.22300\teval-mlogloss:0.22753\n",
      "[525]\ttrain-mlogloss:0.22300\teval-mlogloss:0.22753\n",
      "[526]\ttrain-mlogloss:0.22298\teval-mlogloss:0.22753\n",
      "[527]\ttrain-mlogloss:0.22297\teval-mlogloss:0.22753\n",
      "[528]\ttrain-mlogloss:0.22296\teval-mlogloss:0.22753\n",
      "[529]\ttrain-mlogloss:0.22295\teval-mlogloss:0.22754\n",
      "[530]\ttrain-mlogloss:0.22294\teval-mlogloss:0.22754\n",
      "[531]\ttrain-mlogloss:0.22292\teval-mlogloss:0.22754\n",
      "[532]\ttrain-mlogloss:0.22291\teval-mlogloss:0.22754\n",
      "[533]\ttrain-mlogloss:0.22290\teval-mlogloss:0.22754\n",
      "[534]\ttrain-mlogloss:0.22289\teval-mlogloss:0.22754\n",
      "[535]\ttrain-mlogloss:0.22288\teval-mlogloss:0.22754\n",
      "[536]\ttrain-mlogloss:0.22286\teval-mlogloss:0.22754\n",
      "[537]\ttrain-mlogloss:0.22285\teval-mlogloss:0.22754\n",
      "[538]\ttrain-mlogloss:0.22284\teval-mlogloss:0.22754\n",
      "[539]\ttrain-mlogloss:0.22283\teval-mlogloss:0.22754\n",
      "[540]\ttrain-mlogloss:0.22282\teval-mlogloss:0.22755\n",
      "[541]\ttrain-mlogloss:0.22281\teval-mlogloss:0.22755\n",
      "[542]\ttrain-mlogloss:0.22280\teval-mlogloss:0.22755\n",
      "[543]\ttrain-mlogloss:0.22279\teval-mlogloss:0.22754\n",
      "[544]\ttrain-mlogloss:0.22278\teval-mlogloss:0.22754\n",
      "[545]\ttrain-mlogloss:0.22277\teval-mlogloss:0.22754\n",
      "[546]\ttrain-mlogloss:0.22276\teval-mlogloss:0.22754\n",
      "[547]\ttrain-mlogloss:0.22275\teval-mlogloss:0.22754\n",
      "[548]\ttrain-mlogloss:0.22274\teval-mlogloss:0.22754\n",
      "[549]\ttrain-mlogloss:0.22273\teval-mlogloss:0.22755\n",
      "[550]\ttrain-mlogloss:0.22273\teval-mlogloss:0.22755\n",
      "[551]\ttrain-mlogloss:0.22271\teval-mlogloss:0.22755\n",
      "[552]\ttrain-mlogloss:0.22270\teval-mlogloss:0.22755\n",
      "[553]\ttrain-mlogloss:0.22269\teval-mlogloss:0.22754\n",
      "[554]\ttrain-mlogloss:0.22267\teval-mlogloss:0.22754\n",
      "[555]\ttrain-mlogloss:0.22266\teval-mlogloss:0.22754\n",
      "[556]\ttrain-mlogloss:0.22265\teval-mlogloss:0.22754\n",
      "[557]\ttrain-mlogloss:0.22264\teval-mlogloss:0.22755\n",
      "[558]\ttrain-mlogloss:0.22263\teval-mlogloss:0.22755\n",
      "[559]\ttrain-mlogloss:0.22262\teval-mlogloss:0.22755\n",
      "[560]\ttrain-mlogloss:0.22260\teval-mlogloss:0.22755\n",
      "[561]\ttrain-mlogloss:0.22260\teval-mlogloss:0.22755\n",
      "[562]\ttrain-mlogloss:0.22259\teval-mlogloss:0.22755\n",
      "[563]\ttrain-mlogloss:0.22257\teval-mlogloss:0.22756\n",
      "[564]\ttrain-mlogloss:0.22256\teval-mlogloss:0.22756\n",
      "[565]\ttrain-mlogloss:0.22255\teval-mlogloss:0.22756\n",
      "[566]\ttrain-mlogloss:0.22254\teval-mlogloss:0.22756\n",
      "[567]\ttrain-mlogloss:0.22253\teval-mlogloss:0.22755\n",
      "[568]\ttrain-mlogloss:0.22252\teval-mlogloss:0.22756\n",
      "[569]\ttrain-mlogloss:0.22250\teval-mlogloss:0.22756\n",
      "[570]\ttrain-mlogloss:0.22250\teval-mlogloss:0.22756\n",
      "[571]\ttrain-mlogloss:0.22248\teval-mlogloss:0.22756\n",
      "[572]\ttrain-mlogloss:0.22247\teval-mlogloss:0.22756\n",
      "[573]\ttrain-mlogloss:0.22246\teval-mlogloss:0.22756\n",
      "[574]\ttrain-mlogloss:0.22245\teval-mlogloss:0.22756\n",
      "[575]\ttrain-mlogloss:0.22244\teval-mlogloss:0.22756\n",
      "[576]\ttrain-mlogloss:0.22243\teval-mlogloss:0.22756\n",
      "[577]\ttrain-mlogloss:0.22242\teval-mlogloss:0.22756\n",
      "[578]\ttrain-mlogloss:0.22241\teval-mlogloss:0.22756\n",
      "[579]\ttrain-mlogloss:0.22240\teval-mlogloss:0.22756\n",
      "[580]\ttrain-mlogloss:0.22239\teval-mlogloss:0.22756\n",
      "[581]\ttrain-mlogloss:0.22238\teval-mlogloss:0.22756\n",
      "[582]\ttrain-mlogloss:0.22237\teval-mlogloss:0.22756\n",
      "[583]\ttrain-mlogloss:0.22236\teval-mlogloss:0.22756\n",
      "[584]\ttrain-mlogloss:0.22235\teval-mlogloss:0.22757\n",
      "[585]\ttrain-mlogloss:0.22234\teval-mlogloss:0.22757\n",
      "[586]\ttrain-mlogloss:0.22233\teval-mlogloss:0.22757\n",
      "[587]\ttrain-mlogloss:0.22232\teval-mlogloss:0.22757\n",
      "[588]\ttrain-mlogloss:0.22231\teval-mlogloss:0.22756\n",
      "[589]\ttrain-mlogloss:0.22229\teval-mlogloss:0.22756\n",
      "[590]\ttrain-mlogloss:0.22228\teval-mlogloss:0.22757\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "predict() got an unexpected keyword argument 'ntree_limit'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_20168\\1896354288.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     40\u001B[0m                   \u001B[0mevals_result\u001B[0m \u001B[1;33m=\u001B[0m\u001B[0mscore_dict\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     41\u001B[0m                   )\n\u001B[1;32m---> 42\u001B[1;33m \u001B[0my_train_pred\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpredict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtrain_matrix\u001B[0m \u001B[1;33m,\u001B[0m\u001B[0mntree_limit\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbest_iteration\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;31m#ntree_limit  用于预测的树的数量\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     43\u001B[0m \u001B[0my_train_pred_pos\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0my_train_pred\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     44\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mTypeError\u001B[0m: predict() got an unexpected keyword argument 'ntree_limit'"
     ]
    }
   ],
   "execution_count": 94
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:28.962696Z",
     "start_time": "2024-09-19T11:39:28.763131Z"
    }
   },
   "cell_type": "code",
   "source": "plot_auc_curve(fpr, tpr, auc_test)",
   "id": "da19013ee2b707a6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 95
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:32.756658Z",
     "start_time": "2024-09-19T11:39:32.599881Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "plt.plot(score_dict['train']['mlogloss'], 'r-+', label = 'Training Loss')\n",
    "plt.plot(score_dict['eval']['mlogloss'], 'b-', label = 'Test Loss')"
   ],
   "id": "514421e72a7ee663",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24ca7dac910>]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ],
      "image/png": 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rKUpsEV3HgIArkSRJkiRJSm+7HXqp+4VrqgnXVNM/K/HUxnUMILpg60MDYqVldn1JkiRJkiTthi6f6aV9J3d2FcWVFQya/zgAaxlI4cwzKa6soLiygtzZVQFXKEmSJEmSlF7s9EoBTVOn0TJ5CkU/Xwf3wRr2o/6mn9BWPh7ALi9JkiRJkqTdZOiVAjq3L5YcsSQZerWVj6etfELQpUmSJEmSJKUltzemkP4HFgCJ0EuSJEmSJEl7ztArhfQf2Q+A9QygJa8o2GIkSZIkSZLSmKFXCuk3JJ8w7QCsr88JuBpJkiRJkqT0ZeiVQiLREAPCGwBYv7Qu4GokSZIkSZLSl6FXitkveyMA69/aEmwhkiRJkiRJaczQK8UMzG8AYN2qloArkSRJkiRJSl+GXilmYGGiw2tddXvAlUiSJEmSJKUvQ68UM6BfGwDr1oUCrkSSJEmSJCl9GXqlmAEDYgCsq80KuBJJkiRJkqT0ZeiVYgaURQBYU58XcCWSJEmSJEnpy9ArxQwYkujwWru5T8CVSJIkSZIkpS9DrxTTf3g+AGtb+gZciSRJkiRJUvoy9Eox/UcUAbAmNoB4m09wlCRJkiRJ2hOGXimm/8GJDq8t5LN55cZgi5EkSZIkSUpThl4ppqAoSj6NAGx4fnmwxUiSJEmSJKUpQ68UVBpZD8CGl2sCrkSSJEmSJCk9GXqloIE5mwBYVxMLuBJJkiRJkqT0FA26ACWEa6oJ11QDMDCaCLs2vLGJ6IJ5AMRKy4iVlgVVniRJkiRJUlqx0ytF5M6uoriyguLKCsrqXgegdt47yddyZ1cFXKEkSZIkSVL6sNMrRTRNnUbL5CkAlHx9FbwJq0vHU/ubuQB2eUmSJEmSJO0GQ68Use32xQFDN8CbsKathLbyCcEWJkmSJEmSlIbc3piCBgyMA7B2S2HAlUiSJEmSJKUnQ68U1H9kXwDWtvQLthBJkiRJkqQ0ZeiVgkrGDQIS2xslSZIkSZK0+wy9UlD/g/sBsI7+tNc1BluMJEmSJElSGjL0SkHFQ/MJESNOmNqltUGXI0mSJEmSlHYMvVJQNCtE/3Ai7NrwZn3A1UiSJEmSJKUfQ68UtV92IvRa/9bmgCuRJEmSJElKP4ZeKWpgXgMA61a2BFyJJEmSJElS+jH0SlEDC5sAWFfdHnAlkiRJkiRJ6cfQK0UN7NcKwLp1oYArkSRJkiRJSj+GXilqwIAYAGs3ZAVciSRJkiRJUvox9EpR/UsTvzVr63MDrkSSJEmSJCn9GHqlqIFDswFYu7lPwJVIkiRJkiSlH0OvFNV/WB4Aa5uLCNdUB1yNJEmSJElSejH0SlH9DyoCYE18IOFVqwKuRpIkSZIkKb0YeqWoztCrkT40rm0MuBpJkiRJkqT0Eg26AG0vXFNNuKaavnHI5SiayGPT3FcYXFYIQKy0jFhpWcBVSpIkSZIkpTY7vVJM7uwqiisrKPloBaXUAFB/+30UV1ZQXFlB7uyqgCuUJEmSJElKfXZ6pZimqdNomTwFgP0+vom3muGtii8w+tIfANjlJUmSJEmS1AWGXilm2+2LZYULoRlqYvvRVj4h2MIkSZIkSZLSiNsbU1hpUeIA++r1WQFXIkmSJEmSlF4MvVLYfqWJsaahT7CFSJIkSZIkpRlDrxS234F5AFQ3FAZciSRJkiRJUnox9Eph+w3PAaBmc1HAlUiSJEmSJKUXQ68UNvCgxLbG6paSgCuRJEmSJElKL4ZeKWy/UX0BqInvR6xxS8DVSJIkSZIkpQ9DrxQ2YESi06uNLDYuWRtwNZIkSZIkSenD0CuFZWWHGBBeD8DaJfUBVyNJkiRJkpQ+DL1SXFlOLQBr3mwMuBJJkiRJkqT0YeiV4soKEh1ea95pCbgSSZIkSZKk9GHoleLK+iUOsF+zOh5wJZIkSZIkSenD0CvF7TegHYCatZGAK5EkSZIkSUofhl4pbr9BIQDWrmwjXFMdcDWSJEmSJEnpwdArxZXuHwWgurHQ0EuSJEmSJKmLDL1S3MAD8wFYzaCAK5EkSZIkSUof0aAL0M6Fa6oJ11RTGmoGEqFX9KVHktdjpWXESsuCKk+SJEmSJCmlGXqlqNzZVRTceB2HUAA00EgfQt+9mGIaAWg87wI2n39RsEVKkiRJkiSlKEOvFNU0dRotk6cAUFCZCL2WfuVShn3xOAC7vCRJkiRJknbB0CtFbbt9cVDWOt5o7cPq8BAGl08ItjBJkiRJkqQ04EH2aaAstxaANT68UZIkSZIkqUsMvdLAfkVbAKjZlBdwJZIkSZIkSenB0CsNlO4XAwy9JEmSJEmSusrQKw2U7hcHoGZDdsCVSJIkSZIkpQdDrzQwcEjieQM19QUBVyJJkiRJkpQeDL3SwH4H5ABQvaVvwJVIkiRJkiSlB0OvNLDfwYUArG4dAPF4wNVIkiRJkiSlPkOvNDBgdDEA6xhI27qNwRYjSZIkSZKUBgy90kD/wTlEaQVg3eINAVcjSZIkSZKU+gy90kA4DKXR9QCsfb0u4GokSZIkSZJSn6FXmijL3QjA2uVbgi1EkiRJkiQpDRh6pYnSPo0ArP/bIsI11QFXI0mSJEmSlNoMvdJEaXETkNjeaOglSZIkSZK0a4ZeaaJ0vxgA1ZQFXIkkSZIkSVLqiwZdgHYtXFNNuKaa0o4zvVYziOiC+cnrsdIyYqUGYZIkSZIkSduy0yvF5c6uoriyggMe+yWQCL0KZ55JcWUFxZUV5M6uCrhCSZIkSZKk1GOnV4prmjqNlslTKJq7Ga6AlQyh/oe30DZ+AoBdXpIkSZIkSTth6JXiOrcvDixsgisSZ3o1HzCSePmEoEuTJEmSJElKWW5vTBMDh+USoY12oqx7syHociRJkiRJklKaoVeaiERgUHQtAKvW5QRcjSRJkiRJUmoz9Eojg/M3AlC9NivYQiRJkiRJklKcoVcaGdQ3sa1x1dvtAVciSZIkSZKU2gy90sjggS0AVK/2t02SJEmSJGlXTE/SyKBBiXHl+txgC5EkSZIkSUpxhl5pZNCBibO8Vtf1CbgSSZIkSZKk1GbolUZKDyoAYFVT/4ArkSRJkiRJSm2GXmmkbGw/AFbEBhHf0hRsMZIkSZIkSSnM0CuNlB5cCMAW8qlfUhNwNZIkSZIkSanL0CuN5OWHKAnXAlD7w3sI11QHXJEkSZIkSVJqMvRKM0Ny1wOw7i/zDL0kSZIkSZLeg6FXmhlcWA/ASoYEXIkkSZIkSVLqigZdgLomXFNNuKaawQWtAKxgKNEF85PXY6VlxErLgipPkiRJkiQppdjplSZyZ1dRXFnB8DefAhKdXoUzz6S4soLiygpyZ1cFXKEkSZIkSVLqsNMrTTRNnUbL5CkM+PEmeCjR6VV/009oKx8PYJeXJEmSJEnSNgy90kTn9sX9jnkTHkp0erWVF9JWPiHo0iRJkiRJklKO2xvTTNkhfYFEpxftsYCrkSRJkiRJSk2GXmmmbEwxABvoz+b2rICrkSRJkiRJSk2GXmmmb0mYvNAWAGqqQwFXI0mSJEmSlJoMvdJMKARDstcAsPq1xoCrkSRJkiRJSk2GXmlocJ86AKqXNQVciSRJkiRJUmoy9EpDg4s3A7B6RTzgSiRJkiRJklKToVcaGrRfGwCrayIBVyJJkiRJkpSaDL3S0KChid+2VRsLAq5EkiRJkiQpNRl6paFBI3IAWNVQFHAlkiRJkiRJqcnQKw2VjuoDwKqWgRD3XC9JkiRJkqQdGXqlobKx/QFYHS8j/uprAVcjSZIkSZKUegy90tDA/bOJ0EY7UTb8e1nQ5UiSJEmSJKUcQ680FInAoMhaAFYvaw64GkmSJEmSpNQTDboAdV24pppwTTUAg7OKWNE+iDXz1xBdMA+AWGkZsdKyACuUJEmSJElKDbvV6bVkyRJOO+00Jk6cyPXXX0+8C4eoT58+ndGjRyd/ffnLX97TWnu93NlVFFdWUFxZwf5NrwNQ839vJV/LnV0VcIWSJEmSJEmpocudXi0tLUyfPp3jjz+em2++mauuuoo5c+Zw2mmn7fK+RYsW8dBDD1FWluhAikZtLttTTVOn0TJ5CgCDz1gPy2B56THU/uZzAHZ5SZIkSZIkdehyAjV37lwaGhq48MILycvLY+bMmcyaNWuXoVd1dWIr3qhRo/a60FBor79ESuh8H3vyfuJlZbR3hIdDD3oalsHbTQNpHz9h69fvhhqlrtqb9SylEteyMoVrWZnCtaxM4VpWpkiltbw7NXQ59Fq8eDHjx48nLy8PgNGjR7N06dJd3rNgwQLa29upqKigrq6OE088kcsvv5y+fft2vcIO/fsX7vY9qWxv38/Bo7LhCVixeQADBmTWz0bpJ9P+fKr3ci0rU7iWlSlcy8oUrmVlinRby10OvRoaGhg6dGjy81AoRDgcZtOmTe8ZYi1fvpwxY8bwve99j3A4zIUXXshNN93ErFmzdrvQ9evr6cIRYikvFEoskr19P/3GlADwdmsZ62o2Jh7pKPWw7lrPUtBcy8oUrmVlCteyMoVrWZkildZyZy1d0eXQKxKJkJ2dvd1rOTk5NDU1vWfo9fWvf52vf/3ryc/PO+88zjrrrD0KveJxAv/Bdqe9fT9lFSMAWM1gWt5ZQtbwQd1UmbT7Mu3Pp3ov17IyhWtZmcK1rEzhWlamSLe13OWnN/bt25cNGzZs91pjYyNZWVld/mZFRUXU1tbS0tLS9Qq1U/33i5AX2gJA9fy1AVcjSZIkSZKUWroceo0bN4758+cnP1+xYgUtLS27PJ/rrLPOYt68ecnPFy5cyMCBA9/VMabdFwrB/jlrAFj1Sl3A1UiSJEmSJKWWLodeEydOpL6+ngceeACA22+/nUmTJhGJRGhoaKC1tfVd94waNYprr72W+fPn8+STT/LjH/+YL3zhC91WfG+3f9EmAFa8YeecJEmSJEnStrp8plc0GuXKK6/k3HPP5YYbbqC9vZ177rkHgFNOOYWLLrqIysrK7e75xje+wapVq/jKV75C//79+fznP883vvGN7n0Hvdj++22BNbDynaArkSRJkiRJSi1dDr0AKisr+etf/8rChQs54ogjKClJPEHw73//+07nZ2Vlcc0113DNNdfsfaV6lyGDY7AIVqzJCboUSZIkSZKklLJboRdAaWkppaWl+6IW7aYhByUeIvDOmjzCNdXESssCrkiSJEmSJCk1dPlML6WeIYf2AeCd1jLCNdUBVyNJkiRJkpQ6DL3S2KDyxPbStxlGvL4h4GokSZIkSZJSx25vb1TwwjXVhGuq2b8lRIgBNJFH3RMv0rcw0fkVKy1zq6MkSZIkSerV7PRKQ7mzqyiurKBsygcpI7Gtcd1P/0hxZQXFlRXkzq4KuEJJkiRJkqRg2emVhpqmTqNl8hQAhn1yLau3DOaNY7/AQVfdDGCXlyRJkiRJ6vUMvdLQttsXh5Ys4LmV8E7TfrSVTwi2MEmSJEmSpBTh9sY0N7T/FgDeWZcfcCWSJEmSJEmpw9ArzQ0dkWjWe6e+X7CFSJIkSZIkpRBDrzQ3eHwJACs29w+4EkmSJEmSpNRh6JXmBo3tB8DbrYOgtTXYYiRJkiRJklKEoVeaGzquLwBrKKV52eqAq5EkSZIkSUoNhl5prm9xmD6hBgCqF6wPuBpJkiRJkqTUYOiV5kIh2D9vLQArX60PuBpJkiRJkqTUYOiVAYb2rQNg5VLP9JIkSZIkSQJDr4yw/35NAKz6Tw3hmuqAq5EkSZIkSQqeoVcGGDo0DsDKNdmGXpIkSZIkSRh6ZYTBB2UD8DbDAq5EkiRJkiQpNUSDLkB7LlxTTbimmmEFie2NbzGc6PxHk9djpWXESsuCKk+SJEmSJCkwhl5pLHd2FQU3XsdhDAPe4h32p+DcswmT2O7YeN4FbD7/omCLlCRJkiRJCoChVxprmjqNlslTyG+H6MmttJDDG9MuZ+B/nwhgl5ckSZIkSeq1DL3S2LbbF4dlV/Nmy/682TKU4vIJwRYmSZIkSZIUMA+yzxAH9FkHwDvLg61DkiRJkiQpFRh6ZYjhZVsAWL6uT8CVSJIkSZIkBc/QK0MMOzixU/Wt9X0DrkSSJEmSJCl4hl4ZYv/RuQAsrysOuBJJkiRJkqTgGXpliGGH9wNgWfMQaG0NthhJkiRJkqSAGXpliP3HJzq8VjGElqUrAq5GkiRJkiQpWIZeGaKkPxSGGwBY+eK6gKuRJEmSJEkKlqFXhgiF4ID8NQBUz36ScE11wBVJkiRJkiQFx9ArgxzQfxMA77y0wdBLkiRJkiT1aoZeGWTY4MQB9ss4MOBKJEmSJEmSghUNugDtvXBNNeGaag4o3gLAm4wgumB+8nqstIxYaVlQ5UmSJEmSJPU4O70yQO7sKoorKzj0kR8DiU6vwplnUlxZQXFlBbmzqwKuUJIkSZIkqWfZ6ZUBmqZOo2XyFPq/FoJvJTq96q6+gfZjjgWwy0uSJEmSJPU6hl4ZoHP74uCDgW9BHX3ZkDuYgvIJQZcmSZIkSZIUCLc3ZpC8PBgUXQPAW4ubA65GkiRJkiQpOIZeGWZ4n3UAvL06J+BKJEmSJEmSgmPolWEOKEs8wfGt6tyAK5EkSZIkSQqOoVeGGbZ/OwBv2eklSZIkSZJ6MUOvDDN8VBYAy2v7BVuIJEmSJElSgAy9MszQcX0BWL6lDGKxgKuRJEmSJEkKhqFXhtn/iBIA3ooPI/fSSwjXVAdckSRJkiRJUs8z9Mowg/aPkEULrWRTe/uDhl6SJEmSJKlXMvTKMJEIDMtdA8AyDgy4GkmSJEmSpGBEgy5A3SdcU024ppoD+xSwtGkobzKCIxbMT16PlZYRKy0LsEJJkiRJkqSeYadXBsmdXUVxZQUHrfs3AG8ygsKZZ1JcWUFxZQW5s6sCrlCSJEmSJKln2OmVQZqmTqNl8hSGXtcMT8AbjKT+pp/QVj4ewC4vSZIkSZLUaxh6ZZDO7YvDP7QSnoDXOZi28qG0lU8IujRJkiRJkqQe5fbGDHTAMQOAROgV37gp4GokSZIkSZJ6nqFXBjrgkBxCxKijL+tr2oMuR5IkSZIkqccZemWg3FzYP2cNAMveCLgYSZIkSZKkABh6ZaiDitcDsOyV5oArkSRJkiRJ6nmGXhlqxJAmAJYt97dYkiRJkiT1PiYiGerAUYnf2qXVRQFXIkmSJEmS1PMMvTLUgeMLAFi6aSDh6tUBVyNJkiRJktSzDL0y1AFHDwDgdUYSevXVgKuRJEmSJEnqWYZeGWr4qCzCtNNAIesXbwi6HEmSJEmSpB4VDboAda9wTTXhmmqiwPDoASxr25+3nlrBgA/MAyBWWkastCzQGiVJkiRJkvY1O70yTO7sKoorKyiurODgtlcAePvvy5Kv5c6uCrhCSZIkSZKkfc9OrwzTNHUaLZOnAHDg1xpgOSweeDy1/+8rAHZ5SZIkSZKkXsHQK8Nsu33xgEOfg+WwdPNg2sonBFqXJEmSJElST3J7YwYbMToCwBubB0M8HnA1kiRJkiRJPcfQK4MNrxgKwBvxgwitXh1wNZIkSZIkST3H0CuD7X9MGRHa2EwBa/6zIuhyJEmSJEmSeoyhVwbLyoLheTUALH9hU8DVSJIkSZIk9RxDrwx3UP9aAJa92hJwJZIkSZIkST3H0CvDjdi/GYBl8+oJ11QHXI0kSZIkSVLPMPTKcJ1PcFy6cYChlyRJkiRJ6jUMvTLcAROKAHidgyEWC7gaSZIkSZKknhENugDtG+GaasI11YwsygJGsJSDiDx1P4QTOWestIxYaVmwRUqSJEmSJO0jdnplqNzZVRRXVlA+7XiyaKGJPGqvTrxWXFlB7uyqoEuUJEmSJEnaZ+z0ylBNU6fRMnkKACM+voLXmkew8LivUnTliQB2eUmSJEmSpIxm6JWhtt2+OHrga7y2YgSLNw3imPIJwRYmSZIkSZLUA9ze2AscPLQRgCXV/YItRJIkSZIkqYcYevUCI48oAGBJ3aCAK5EkSZIkSeoZhl69wEEnHQDAq20HE9qwPthiJEmSJEmSeoChVy8wsjwHgDWUUvfisoCrkSRJkiRJ2vcMvXqBPn1gaM4aAN7+0Z8J11QHXJEkSZIkSdK+ZejVS4wakNjW+Oa/aw29JEmSJElSxjP06iUOPqAZgFc5NOBKJEmSJEmS9r1o0AVo3wrXVBOuqWZ0WRuQCL2iC+Ynr8dKy4iVlgVVniRJkiRJ0j5h6JXhcmdXUXDjdUzgBOBEFnMIhTM/mbzeeN4FbD7/osDqkyRJkiRJ2hcMvTJc09RptEyewqD1UTgdlnEg68+6mPApJwPY5SVJkiRJkjKSoVeG69y+WBKHfuE6NsaKeGPzIEaUTwi6NEmSJEmSpH3Gg+x7iVAIRhWtAuD11wIuRpIkSZIkaR8z9OpFRg9pAOC1VX0DrkSSJEmSJGnfMvTqRQ4uzwbg9bUlAVciSZIkSZK0bxl69SIjjioCYHH9EGhrC7gaSZIkSZKkfcfQqxcZOak/AK/FR5F3yUWEa6oDrkiSJEmSJGnfMPTqRYYNh+xQC03ksbbqL4ZekiRJkiQpYxl69SLRKBxcuBqAxRwScDWSJEmSJEn7TjToAtQzwjXVhGuqGV2cy8t1w3mVQzl+wfzk9VhpGbHSsgArlCRJkiRJ6j52evUSubOrKK6sYMxbjwLwKodSOPNMiisrKK6sIHd2VcAVSpIkSZIkdR87vXqJpqnTaJk8hQP+EIJfwCscRv0Pb6Ft/AQAu7wkSZIkSVJGMfTqJTq3L44Mx+AXsIixtPV/h7byCUGXJkmSJEmS1O3c3tjLHDQqTJRW6ujL6nnrgi5HkiRJkiRpnzD06mWys2F04SoAFr+eFXA1kiRJkiRJ+4ahVy906LAGAF5Zlh9wJZIkSZIkSfuGoVcvdMjYxPjKO32DLUSSJEmSJGkfMfTqhQ6ZlAi7Xq4fTvjttwKuRpIkSZIkqfsZevVCo49LhF6LOYT4s88HXI0kSZIkSVL3M/TqhfYfBn3CjbSQw/KX6oIuR5IkSZIkqdtFgy5APSdcU024phqAMQXFPFc/lsXP1HPQgnkAxErLiJWWBVihJEmSJElS97DTqxfJnV1FcWUFxZUVjKt/BoDXXoknX8udXRVwhZIkSZIkSd3DTq9epGnqNFomTwFg1Kx18DQsiB5B7V/mAtjlJUmSJEmSMoahVy+y7fbFUR99BZ6GRW2H0D4si3i/4oCrkyRJkiRJ6j5ub+ylDjssDsCbHETTvNcDrkaSJEmSJKl7GXr1UiWj+1MaWQvA63NrAq5GkiRJkiSpexl69VKx0jIOHVQLwOIXWwKuRpIkSZIkqXsZevVih41qBmDx/DbCNdUBVyNJkiRJktR9DL16sUOOzAPg5cYDCFevDrgaSZIkSZKk7mPo1YuNqhgIwCLGElq3NuBqJEmSJEmSuk806ALU88I11YRrqhlDGPgANZSx6eFnKRq4H5A47ytWWhZskZIkSZIkSXthtzq9lixZwmmnncbEiRO5/vrricfjXb63tbWVT37ykzz33HO7XaS6V+7sKoorKxj6yeMZwVIAlt7zAsWVFRRXVpA7uyrgCiVJkiRJkvZOl0OvlpYWpk+fzpgxY7j//vtZunQpc+bM6fI3uvPOO1myZMkeFanu1TR1GrVPzKX2ibmMLUtsa3xh0MeTrzVNnRZwhZIkSZIkSXuny9sb586dS0NDAxdeeCF5eXnMnDmTWbNmcdppp73vvcuXL6eqqoohQ4bscaGh0B7fmlI630eQ7ydeVkZ7WWL74rjyhfypGuZvHE77+AnJORny49Y+lgrrWeoOrmVlCteyMoVrWZnCtaxMkUpreXdq6HLotXjxYsaPH09eXuKJf6NHj2bp0qVduvfSSy/ljDPO4Omnn+56ZTvo379wj+9NRanyfo79UB/4K8zbcggDsmLQt2/QJSkNpcp6lvaWa1mZwrWsTOFaVqZwLStTpNta7nLo1dDQwNChQ5Ofh0IhwuEwmzZtou8ugpL777+fhoYGpk2btleh1/r19ezGEWIpKxRKLJJUeT8HHNMHgMUcwurH/kbWhycFXJHSSaqtZ2lPuZaVKVzLyhSuZWUK17IyRSqt5c5auqLLoVckEiE7O3u713JycmhqanrP0GvDhg3cdNNN3HnnnUSje/egyHicwH+w3SlV3s9+4wYyMHsja1v68eYNf+KQw0b45EbttlRZz9Leci0rU7iWlSlcy8oUrmVlinRby10+yL5v375s2LBhu9caGxvJysp6z3uuvvpqPvvZz3LooYfueYXap0IhKB+cOMx+0QtthGuqA65IkiRJkiRp73U59Bo3bhzz589Pfr5ixQpaWlp2ubXxz3/+M3fffTdHHXUURx11FC+88ALTp0/n9ttv37uq1a3GjW0D4CUOD7gSSZIkSZKk7tHlPYcTJ06kvr6eBx54gE9/+tPcfvvtTJo0iUgkQkNDAzk5Oe/q+vrb3/623eczZ85k6tSpfPCDH+ye6rVXwjXVhGuqGXtgYtvqSxxO9D//Sl6PlZa51VGSJEmSJKWlLode0WiUK6+8knPPPZcbbriB9vZ27rnnHgBOOeUULrroIiorK7e7Z9uD7yFxBtiAAQMoKirqhtK1t3JnV1Fw43UczyjgNRZQTt4FHyBKOwCN513A5vMvCrZISZIkSZKkPRCKx3fvCLKamhoWLlzIEUccQUlJyb6q613WrQv+CQHdIRSCAQMKU+L9dHZ6xWIw/OQjaIwX8OLJ53HAdz8F2Oml95dK61naG65lZQrXsjKFa1mZwrWsTJFKa7mzlq7Y7UcqlpaWUlpauttFKfVsG2qNK1nJs+tHMa9mMEPLJwRbmCRJkiRJ0l7q8kH2ymzlB2wEYOHbxcEWIkmSJEmS1A0MvQTAmEn5AMyvPQDa2oItRpIkSZIkaS8ZegmAMZ8cDsBL8fEUXHg+4ZrqgCuSJEmSJEnac4ZeAmD0oRANtVFLCWtn/9XQS5IkSZIkpTVDLwGQkwOHFq8G4CUOD7gaSZIkSZKkvbPbT29U5gnXVBOuqaZ8UB4LN+zPSxzORxbMT17f9imPkiRJkiRJ6cBOL5E7u4riygqOfvnXQKLTq3DmmRRXVlBcWUHu7KqAK5QkSZIkSdo9dnqJpqnTaJk8hZHzCuA8eJEjaLzgEloqTwKwy0uSJEmSJKUdQy8lty8eOgLC57WzkqGsri+gb/mEoEuTJEmSJEnaI25vVFKfPnBo0UoA5j0fD7gaSZIkSZKkPWfope0cMboOgBeW9Q+4EkmSJEmSpD1n6KXtlH+4HwAvrDuQ/GuvJFxTHWxBkiRJkiRJe8DQS9s5vLIfAM/HjyL/5h8YekmSJEmSpLRk6KXtHHpYnJxwCxsp5g1GBl2OJEmSJEnSHvHpjUoK11STV1PN+JJB/HvdSP7N0ZyyYH7yeudTHiVJkiRJklKdnV5Kyp1dRXFlBceuexiA55lI4cwzKa6soLiygtzZVQFXKEmSJEmS1DV2eimpaeo0WiZPYcyf8uEW+DdHU3/1DbQdcyyAXV6SJEmSJCltGHopqXP7YnlBCG6Blzic1sh/aC+fEHRpkiRJkiRJu8XtjXqXAw+M0y9aTxN5LH6mLuhyJEmSJEmSdpuhl94lHIYjhqwGYN4/mwjXVAdckSRJkiRJ0u4x9NJOTTg6AsAL6w809JIkSZIkSWnH0Es7Nb6yBEg8wTG0bm3A1UiSJEmSJO0eD7LXdsI11YRrqpnYLws4lpcZQ8uf7yBrwEBg62H3kiRJkiRJqcxOL20nd3YVxZUVHHr6cQxhBTEiLL5nPsWVFRRXVpA7uyroEiVJkiRJkt6XnV7aTtPUabRMngLAkV9ezcoVQ3mmeArj/nA1gF1ekiRJkiQpLdjppe3ESstoK59AW/kEjpqUOMz+2Y2H0jb6UNrKJxh6SZIkSZKktGDopfd01HGJ5fF/8WOJzJ8XbDGSJEmSJEm7wdBL76n8g33IpoU1lFJ9xV2Ea6qDLkmSJEmSJKlLDL30nrKHlXL40BoAnv93xNBLkiRJkiSlDUMv7dLEo9sB+D8mQTwecDWSJEmSJEldY+ilnQrXVBNdMI9jDtsEwL/4ANl/f4LognlEF8yz60uSJEmSJKU0Qy/tVO7sKoorKzjpqo8C8DJjabn2JxRXVlBcWUHu7KqAK5QkSZIkSXpv0aALUGpqmjqNlslTyAIO+uRKlm4ZwtOjv8IHf/oZAGKlZcEWKEmSJEmStAuGXtqpWGlZMtg6ZvQyls4bwrOrD+S48gnBFiZJkiRJktQFbm/U+5p4TOIA+2fqxhKq3RBwNZIkSZIkSe/P0Evva+LkfgA8xzHknP89D7GXJEmSJEkpz9BL72vkcSX0y2pkC/m89uAbhl6SJEmSJCnlGXrpfYXDcPTItQD8iw8EXI0kSZIkSdL7M/TSLoVrqokumMcxh24EEqFX9MX/EF0wj+iCeXZ9SZIkSZKklGTopV3KnV1FcWUFH55zDpAIvfqcP5PiygqKKyvInV0VbIGSJEmSJEk7EQ26AKW2pqnTaJk8hYObwkQ/0coqhrC4Yhpll34ZgFhpWbAFSpIkSZIk7YShl3YpVlpGrLSMbOCoQSt4dvWB/HPZMD5dPiHo0iRJkiRJkt6T2xvVZccf2QjA0ysOIv+aKzzPS5IkSZIkpSxDL3XZcaf0A+Af8Qryf3SjoZckSZIkSUpZhl7qsiNP6ktWqJV3GMabjAi6HEmSJEmSpPdk6KUuCddUU/TGPI4a9A4A/+AEogvmE10wj+iCeXZ9SZIkSZKklGLopS7JnV1FcWUFH1n1GwCe5EQKZ55JcWUFxZUV5M6uCrhCSZIkSZKkrXx6o7qkaeo0WiZP4cgX+sL3Ep1ejdO/Tctn/wtIPOVRkiRJkiQpVRh6qUtipWXESss4fCRkX9DCyvhQlq3KpbR8QtClSZIkSZIkvYvbG7Vb8vPhqMErAfjnc3nk33CN53lJkiRJkqSUY+il3faBijgAT685hIIbrzP0kiRJkiRJKcfQS7vtuM+WAvCP+IeIB1yLJEmSJEnSzniml3ZLuKaaY3LXkB2ayKr4EF7nYAYtmJ+83nn2lyRJkiRJUpDs9NJuyZ1dxeCPH89x8f8DEk9xLJx5JsWVFRRXVpA7uyrgCiVJkiRJkuz00m5qmjqNlslTOPa2ATx1P/yNj/CFK0bQNukDAHZ5SZIkSZKklGCnl3ZLrLSMtvIJHP/l4QA8QSWxLc20lU+grXyCoZckSZIkSUoJhl7aI0cc0U5RdDMb6M+rv57nExwlSZIkSVJKMfTSHsnKgg+OXQvA31YeRrh6dcAVSZIkSZIkbWXopT1W8bkSAP7KSYSXLwu4GkmSJEmSpK0MvbTbwjXVRBfM46P7Lwbg/5hEy5y/EF0wj+gCtzpKkiRJkqTgGXppt+XOrqK4soLDv3Q0B/EGbWTxzKONFFdWUFxZQe7sqqBLlCRJkiRJvVw06AKUfpqmTqNl8hQATvzuepa+NJLHQpP54ENnQW6OT3CUJEmSJEmBs9NLuy1WWkZb+QTayidwwqf7APB4vJK8X91BrLTM0EuSJEmSJAXO0Et75fgJm4jQxuuMYvX9//Y8L0mSJEmSlBIMvbRX+owYyDElS4DEUxwlSZIkSZJSgaGX9li4pppwTTUnTI4AidAr66l/+BRHSZIkSZIUOEMv7bHOpzh+6rf/C8Df+Ai5V87yKY6SJEmSJClwPr1Re6zzKY4j2qHkE3VsaO3HP4d/gfG//DqAB9pLkiRJkqTA2OmlPdb5FMf44RP48OHrAHj07XFkP/SgT3GUJEmSJEmBMvRStzj5I80A/Dn+cQp+/EPP85IkSZIkSYEy9FK3OPHj2URo4xXG8CYHBl2OJEmSJEnq5Qy9tNfCNdWUNK1i0ojVADzEJ4nOn+dTHCVJkiRJUmAMvbTXOp/i+Ok3bwYSoVfhuWf5FEdJkiRJkhQYn96ovdb5FMcPrsiDL8NTfIi1J3ya6CUzAZ/iKEmSJEmSep6dXtprnU9xPGDKaEaWrKWNLP62cBDZf3nEpzhKkiRJkqRAGHqpW518fB0Aj6w/loIbr/M8L0mSJEmSFAhDL3Wrkz6dA8AjTKHd5SVJkiRJkgLimV7qNuGaao4bUk1xTjHrmwfwDMcxfsH85HW3OkqSJEmSpJ5iK466Te7sKgaeXMHHmh8AOp7iOPNMn+IoSZIkSZJ6nJ1e6jadT3E88cmB/PZqeJBPcdkp/ybUt4jmT5xC+2Fjgy5RkiRJkiT1EnZ6qdt0PsXxw9OGkB1u5TUOYcmidvLuvot4/wFubZQkSZIkST3G0EvdrrAQPlxeA8ADyw4PuBpJkiRJktQbGXqp24VrqvnUpETodX/8VACiC+YTXTCP6IJ5hGuqgyxPkiRJkiT1AoZe6na5s6s4/WeVZNHCIsaxmNEeaC9JkiRJknqUB9mr2zVNnUZ48hROOGctjy8awv2cxllX96PtmGMBPNtLkiRJkiTtc3Z6qdt1Hmj/8a+VAHAfnyXnkYeSrxt6SZIkSZKkfc3QS/vM5MltRELtzONw3v7XKs/ykiRJkiRJPcbQS/tMSQl8sHwDAPdzGjQ2BlyRJEmSJEnqLQy9tE+Ea6qJLpjHKSfWAoktjrm//38+wVGSJEmSJPUIQy/tE7mzqyiurOCLPzqeMO38h4nU/OYpn+AoSZIkSZJ6hE9v1D7RNHUaLZOnkAUc/9W3mPvWCH7H6Zz92bdo/tzptB82NugSJUmSJElSBrPTS/tE55Ma28on8JlT2wH4LV8k7757ifcf4BMcJUmSJEnSPmXopX3uUxXryKaZhZSzgHFBlyNJkiRJknoBQy/tU+GaaopbavjYkPkA3MP/kP34Yx5oL0mSJEmS9ilDL+1TubOr6Hf6Z5i68loAfst/k3v9tR5oL0mSJEmS9ikPstc+1Xmg/XEtIfp9upGVLUN5suDjfOAzJTR/8lMeaC9JkiRJkvYJO720T3UeaB85ajyf+nAtAL9t/DR598z2QHtJkiRJkrTPGHqpx/zXyesBuI/PsoXcgKuRJEmSJEmZzO2N6hHhmmqOLVvGsNwy3m4q5SE+ycf+83zyeqy0zK4vSZIkSZLUbez0Uo/InV1FyRc+w/803QkknuJYeMG5HmgvSZIkSZL2CUMv9YimqdOofWIun/zlyQA8ysdYsd8EAOpuu4OmqdMCrE6SJEmSJGUaQy/1iM4D7Q/65GgmHryBNrL4zZqTAGg/eLRbGyVJkiRJUrcy9FKPCtdU86VjFgFwJ18jDkQXzCe6YB7RBfMI11QHW6AkSZIkScoIhl7qUbmzq/jyPVPoQz1vcDBP8SEKZ57p2V6SJEmSJKlb+fRG9aimqdMIT57CqT+s59ePFnInX+OY00ohP5/mT5xC+2Fjgy5RkiRJkiRlADu91KM6z/b64jl9AbiPz1I3bzl5d99FvP8Az/aSJEmSJEndwtBLgZgwIcaYYRtpJpffLz066HIkSZIkSVKGMfRSICJrqvnSR94C4A7O8EB7SZIkSZLUrQy9FIjc2VWc8asTyGULCynneSZ6oL0kSZIkSeo2hl4KRNPUafDEnzjlI3UA/JzpANTddge1T8xNXJckSZIkSdpDhl4KROeB9l/6Th8Afst/s47+tI8cRVv5BA+0lyRJkiRJe8XQS4EJ11RzXM6LTBhaQzO53MEZ5Mz5g+d6SZIkSZKkvdYjoVdNTQ0vvvgiDQ0NPfHtlCZyZ1dR8tEKzllxPgA/45tk33ab53pJkiRJkqS9tluh15IlSzjttNOYOHEi119/PfF4/H3vqaqq4hOf+ASXXXYZH/rQh/j3v/+9x8UqszRNnUbtE3M56ZEzGJDfwAr25498Gkic7eW5XpIkSZIkaU91OfRqaWlh+vTpjBkzhvvvv5+lS5cyZ86cXd6zbNkyfvnLX/Lwww/z0EMPMW3aNG655Za9LlqZofNcr+hR45n66Q0A3MqZALQfPNpzvSRJkiRJ0h6LdnXi3LlzaWho4MILLyQvL4+ZM2cya9YsTjvttPe8p729nSuvvJL99tsPgEMOOYS//OUve1RoKLRHt6WczveRKe+nO4Sqq/nauOf5Ef/FXD7EfMoZ8fxzyeux0jLiZQZgqcj1rEzhWlamcC0rU7iWlSlcy8oUqbSWd6eGLodeixcvZvz48eTl5QEwevRoli5dust7Ro4cyciRIwFobGzknnvu4aSTTup6ddvo379wj+5LVZn2fvbKrT+k/6xZnEaE33M6P+FM7rzwjK3XL7sMLr88sPL0/lzPyhSuZWUK17IyhWtZmcK1rEyRbmu5y6FXQ0MDQ4cOTX4eCoUIh8Ns2rSJvn377vLep556inPOOYehQ4cyffr0PSp0/fp6unCEWMoLhRKLJFPeT3cIffaLhD/4Eb68qIjfnwO/4YtcnX81pZuXU3fbHbQe/yHi6+qDLlM74XpWpnAtK1O4lpUpXMvKFK5lZYpUWsudtXRFl0OvSCRCdnb2dq/l5OTQ1NT0vqHXBz7wAe644w6uuuoqfvCDH3DRRRd19dsmxeME/oPtTpn2fvZGvLSMWGkZR46Dw39ax0uvF/GzzV9mFpdvPdvLn1VKcz0rU7iWlSlcy8oUrmVlCteyMkW6reUuH2Tft29fNmzYsN1rjY2NZGVlve+90WiUo446iksuueR9D79X7xVZU805JySe7vkTzqSBAqLzXiK6YB7RBfMI11QHXKEkSZIkSUoXXQ69xo0bx/z585Ofr1ixgpaWll12eT300ENUVVUlP49EIkQikT0sVZkud3YVX7zjZA5mCbWUcAdnUHje2RRXVlBcWUHu7Kr3/yKSJEmSJEnsRug1ceJE6uvreeCBBwC4/fbbmTRpEpFIhIaGBlpbW991z4gRI/jJT37C448/zooVK/jJT37CySef3G3FK7M0TZ1G3RP/4FszE5//kHPZPGAIAHW33UHT1GkBVidJkiRJktJJl0OvaDTKlVdeyWWXXcakSZN47LHHOPfccwE45ZRTeOqpp951z5gxY7j88su59tpr+cxnPsPgwYO58MILu696ZZRYaRlt5RM47TuDKCtuYiVDuXfdRwG2nu0lSZIkSZLUBaF4fPeOIKupqWHhwoUcccQRlJSU7Ku63mXduuCfENAdQiEYMKAwY97PvhCuqea2yzbw/TnHMJrFvMJhtJ50Mlu++nXi/QcQ6zj4XsFzPStTuJaVKVzLyhSuZWUK17IyRSqt5c5auqLLnV6dSktLqays7NHAS71L7uwqzprzUfpRy2scwoN8ipy//oV+p5/q2V6SJEmSJKlLdjv0kva1pqnTaH/iYb763/UAXMUlxIG6n91B7RNzPdtLkiRJkiS9L0MvpZzO7YvTT1tJn+wWXuRIHuRTRP/zPJDY/hiuqQ64SkmSJEmSlMoMvZSScmdXMfK0SZzV8gMALuUKcqvuoLiywi2OkiRJkiTpfRl6KSU1TZ1G7RNz+dovJ1AUaWQh5dzHZwGou+0OtzhKkiRJkqRdMvRSSoqVltFWPoE+n6xgxhfXAXA5l9NOmPYRB/v0RkmSJEmStEuGXkpp4ZpqvjXhHxSzgVc5jN/xeXJ/dgt9zj2LrCef8GwvSZIkSZK0U4ZeSmm5s6vYf+aXOY8bAZjFZWQ9+CB5d99Fv9NP9WwvSZIkSZK0U4ZeSmmdZ3v9z58+Qf+CzbzOKKpInOfl2V6SJEmSJOm9GHoppXWe7VVw4EDO+9h8IPEkx3r6EHn9dfJvuMZtjpIkSZIk6V0MvZQWcmdXMfO+DzGS16mhjBs4n4KbbnCboyRJkiRJ2ilDL6WFpqnT2Hzv77niQ48B8EPO5R2GAG5zlCRJkiRJ72bopbQQKy2j9cSP8NGfnMykkavZQj7f5yoAwtXVbnOUJEmSJEnbMfRSWsn7dRU3v/EpAH7Nl3iJCfSZ9X23OUqSJEmSpO0YeimtNE2dxqh7L+K/DniWOGFmchPtuXmA2xwlSZIkSdJWhl5KK53bHC/8RSm5WW38gxP5fVOi8yvnj3OIvLKI6IJ5bnOUJEmSJKmXM/RSWjr48du5pPUyAGZyE5soIuevj9Lv9FMprqxwm6MkSZIkSb2coZfSUtPUaXztnmM4uGg11QziMmYBUHfr7dQ+MddtjpIkSZIk9XKGXkpLsdIyouMP5bqL1wHwE85kHuPJfuiP5M6uIvLKIrc4SpIkSZLUixl6KW3lzq7i1O+V81/cS4wIM7iN7Mf+4pMcJUmSJEmSoZfSV9PUadQ+MZfLbutLn8hmnuU47uAMAOqvvoFQY4PdXpIkSZIk9VKGXkpbsdIy2sonUHr8QVxy6nwAvssPeJv9yf7ro+TfditZ/3zK4EuSJEmSpF7I0EtpL3d2Fef94Xgm8S/qKeIM7iD7qScBKJpxhtscJUmSJEnqhQy9lPaapk6j/t77+dlJfyA30sJfOZkqEk9v3Py16USWLyPrySfs+JIkSZIkqRcx9FLai5WW0XriRxhZnsdV7RcCMJObeIeh5N/5c3Lvu9eD7SVJkiRJ6mUMvZQxmqZOY9pPD+NYnqGOvpzBHcQ6rtVf9wMPtpckSZIkqRcx9FLGiJWWEauo4Gen/42caBuPMZmfcBYA4eVvebC9JEmSJEm9iKGXMkqstIxR+zdyY9s5AJzPDbzEBAp+fiuQONg+76e3BFihJEmSJEnqCYZeyjhNU6fxxd9V8olBz9NCDqdzLw0UJK+3Dz+A6IJ5dnxJkiRJkpTBDL2UcWKlZbR9+CP89NS/MJR3eJ1RnMlPktcLLzyP4soKD7aXJEmSJCmDGXopYxVM/zx3fP1fhGnnLr7CPXwRgM2f/x+aP/5J2kYebLeXJEmSJEkZytBLGStWWkZF5F9cyhUAfJ3bmcd48n93DzkPP0Tf6V+120uSJEmSpAxl6KWMtuWbZ/GtxyqpPOwdtpDPZ/gj6ykBoP7aHxBqbLDbS5IkSZKkDGTopYwWKy0jfvgEfnHtSg7MX81yDuTz3EsbEXIemEP+bbeS9c+nDL4kSZIkScowhl7qFUqffpAHN59EPo08QSUXczXZzz0DQNGMMyj8+lcMviRJkiRJyiCGXuoVmqZOY/97r+TnR98BwA18j9l8CYDmEz9C9jP/suNLkiRJkqQMYuilXiFWWkbriR/h1IoaLuQaAL7GnfydE8l58m+AHV+SJEmSJGUSQy/1Kk1Tp3H+T/fjdH5HG1mcyhxe5jAAtnzyU2Q/8y/6XHAukUULAq5UkiRJkiTtDUMv9Sqx0jLaKz7E7cfczvE8zSb6MYVHWE0ZeQ89CEDOww+R96s7A65UkiRJkiTtDUMv9Tqx0jJa7/wFd/9gOaN4jbcZzsd4lFr6Jee0Dx1Gn3PPsuNLkiRJkqQ0ZeilXilWWkbh5GN54PDvU0o185nAFB6hnj4A9Ln2CvLuvov8G6/3jC9JkiRJktKQoZd6rVhpGfv9+moe/uDVlLCeZzmOU/gTW8hNzsl95CEPt5ckSZIkKQ0ZeqlXi5WWceCsz/PglFspzG7iH5zIadxPM9kAbPnkZ8h+5l9kP/wgBZddZPglSZIkSVKaMPRSr9c+tpzRd81kzgk3kcdmHmUKn+GPbCGXvIf+CEDhBd8l/7ZbDb0kSZIkSUoThl5ShyMu+BD3H31NMvj6OA/TQAEATR+dDED+zT/wcHtJkiRJktKAoZfUoX1sOUf/cip/HnMefajnST7MyTzGJorIffwvAOQ8/BD5V83yyY6SJEmSJKU4Qy9pG7HSMsb/7jv86Xt/px+1/B8f4ESeZDVlyTm5f3+cvLvvIu9XdwZYqSRJkiRJ2hVDL2kHsdIyyv/nMB757C8YkN/ASxzBcdH/8AqHAtA64iAA2ocO83B7SZIkSZJSlKGXtBOx0jJG/uxb/OOkKziYJbzVNoQP8C+eooKsN5cC0OfaK8i/7Vaf7ChJkiRJUgoy9JJ2YdBZn+Jvn/sJx/Z9mY0UcxJ/ZTZf2m5O55MdI68sIv+Gawy/JEmSJElKAYZe0i60jy0n96fXcN/v6vh02b9oIYcvM5tzuJlWogC0jj8cgOi8Fym48TpDL0mSJEmSUoChl9QF2UeO5c5fx7iUWQD8mHM4mcdYR3+y5r8EQJ9rrwIg/+Yf+GRHSZIkSZICZuglddWgMr43fR33fPFBCmjgST7MkbzA/3HcdtNyHn6I/Ktm0efcswy/JEmSJEkKSDToAqR0ESsto/GKa5hcU82Trbdw+hMzWLZhOBXMZRaXcQHXESEGQO7fH0/ctHkzsQNH0DR1GrHSsgCrlyRJkiSpd7HTS9pNsdIyDrj1TJ781at8gd/STpRLuJqP5D/DCoYk5uTkJMZBgym48ToPuZckSZIkqYcZekl7qM+Igdx+7kLuOOpnFNDAU5uPZiyLuIuphJqbASi49UfA1kPuDb8kSZIkSeoZhl7SHoqVlrHlexdx6g3j+b9PXcVRRYvZRD++wl18gj+zksHJuZ2H3HeGXwZfkiRJkiTtW4Ze0l5qH1vOoDsu5uH713Pl6LvIpplH+DhjeJmf8k3at/lj1hl+5d19F9F/PW34JUmSJEnSPmLoJXWT0PhyvvXTg3iJwzly0Ao20Y9v81OO5t88x9Hvmp97910U3Hid4ZckSZIkSfuAoZfUjWKlZQw/71M8dtN/+CnfpG/OFl7kSI7jGb7KncmD7gFy/vU0sDX86jzvK7JogSGYJEmSJEl7ydBL6kax0jI2n38RjB3D1PP68vxPnmQqdxEnTBVf5WBe50KuYSN9k/d0hl+d531FX3vVc78kSZIkSdpLhl7SPtAZfpUcdzA/Pe81/nr+QxzP0zSRx3VcyEEs5WbOoZns5D2d532F33oLSJz75dMeJUmSJEnaM4Ze0j7UGX4d8b+H8Oi5j/DbGY9zKK+wgf7M5GZG8xq3MZ0mcpL39LnuquTHWU/P3e7cL7c+SpIkSZLUNYZeUg+IlZax5XsXcdI3D+C5L/6QO/kqg/rU8RYH8E1u40CWcSPnUk+f7e4ruPVHwDaH3ndsfbQDTJIkSZKkXTP0knpQrLSMlgsu4PPn7ccLt/2DWziToUUbqWYQ3+VGhrKC73ATb3DQdvd1nvsVXvpmYtywPnnul91fkiRJkiS9m6GX1MM6tzxmTziUaef14YXbnuKXTGPkgA3U0Zcf8R1GsYRP8BB/5aPEt7m3z43XApD9wBwgce5X1nPP+vRHSZIkSZJ2YOglBaQz/IqUH8bp55Xy3E/m8iiT+ejoN4kT5mE+wcn8lUNYzHV8j5UMTt6b+9ijyY9z7v0NAJE333zX0x8NwCRJkiRJvZWhlxSwzvArPnYMHzzvcH5322rmf+oCzuLHFGY3sYTRXMh1DONtJvMov+N0tpCbvD973ksA5P3wegDCy5cnPr/7rnedAWYIJkmSJEnqLQy9pBTRGX61jy1nyFVf5Zrzqnn59if4JdOYNHI1MSI8xmS+wO8YxGqmchcP8Ynkkx+j69YC0Of6q5NfM/JSIhDbsQss8soiCi69iILLLjIIkyRJkiRlJEMvKQV1BmD5R47m9PNKefjq53iDg/jeR/6P4SxnE/34NVM5hYcYyFq+wG+5j9NoJH+7r1Nw+8+AbbrA3ngjMW5YT/7PbyX/tlvtBpMkSZIkZaRo0AVIem+d4Ve4ppqy8/6L86fEuXjC7czt8zH+OutF5nAqK9if3/EFfscXyKGJE/gHU3iEj/EoI3mDENt0gXWEX3k/vSX5PUIbNwFbnwjZftBBFNx4Ha0Tj07MC0HT5z5PziN/pmnqNGKlZT3+c5AkSZIkaXcZeklpoDP8AmgfW86xNdV8+M153Hz3d/jH+X/k0RuWcF/JGSzf0I/HmMxjTOZsYBhvcQL/SP46gOWEgKxFC5Nfu+DyiwHI/eXtAITfeisxdnSDAbSVj08GYbmzq2ie8glyHvkzzVM+AX+eQ/6WFrZ88ywDMUmSJElSyjD0ktJQZwgWKy1jwuQBHBN7louO+gfVn7+QOafdxd/vb+BpPsjbDOfXTOXXTAW2D8GO4xlGsYRwczMA2f95HoA+110F7NANVlsLvLsbrP2gg+Cmm8gHWk748LsCsW1Hu8QkSZIkST3J0EtKU9t2f20eW064ppph532aGVPamJl/D7G7T+GJs/7Av295ib+N+BovvFnyrhCsiE0cxX84mn8zkec5mn8zhJXv6gbrc9H5AORdmwjEsp76x7vq2Vkgtu0Yrqlmy1e+9q5ALPf3v4MQdopJkiRJkrqVoZeUIbYLwc6/iNzSMj44pS+V2Q18d+IzZJ3+RR4/6z6ev+VF/n7QV5m/tIg6+vJ3PsLf+Ujy65SxmsN5iXIWMI6FjGMhh7CYbFrJejux9THvd78BoPCbX0/el/ejmwDI+vsTO60v7+67aJ30gXcFYp1bKHfVKdYZjHm2mCRJkiSpqwy9pAy0sy6wrPPOomJKER/NbuC7E5+lz+mf5WXGMPeLt7LwN6/y7NBTeWVFP6oZxKMM4lGmJL9elFZGsYRRLOFgXt/6K/46g1mV6Ax77VUA8v5wLwB9zpwBQMFllyS/TtafHgQgtHLVu2reVadYV84W2zEYc5ulJEmSJPVuhl5SL7CzEKxl+gwODsH+nwuTM2gZrRP/Sfbp/82/zvsdS298hBcqzmLx3PUsZByb6McrjOEVxrzra+fTyEje4GBeZyRvMIy3E7/a3mZ/3qHfmprk3LxH/wxA4VWXAdDnO2duvfbDGwDInnMfAKFVqxMX4u9+PzsLyHYMxnZ3m2VXwjM7ziRJkiQpfRh6Sb1QrLSMxiuuSX7eGYTFzzubI6f0ZxLFfH7iEvrN/Qxx4NWrf8s7F9/FwtMv4517n+fVMaey7OUmloUPYnOsgAWMZwHjd/q9+lDPMBIB2CBWb/errKk6+XH+G68DkPv4YwAUXnlpYvzmGcmvVXDBdwHI+8F1AGTPuR+AyOJXt37DtrZdvvf32mbZlfCsuzrO9naO56BJkiRJ0vsz9JIE7LwbbPP0b0MIyo4bxvDzjuCYiWvod+851H27gKIZZ7D+ljtZ/+3reZ2DWfjZS1h13/MsG38Kq+av562CQ1nfmE8Dhe/ZJbatIjZRRjUDWEd/1ifHbT8esGkdJWyg75ub6Esf+jz+FwAKfnzT1q/Tsa0yub3yvO8AkH/51m2WOXdVAZD1978BEHntNQBCa9ft1s9sTzvO9nbOnpyDFtSc3bnfEE+SJElSdzL0krRT79UN1njeBbSNPpTG8y4gdNghjJr5SYZuaeEDJ9TQ776zqZueT9GMM6i78Q6iM87i1ctms2HW7bzD/iw/ZQa1f3qWtw//BOtfWsnKkrHUbMhmC/nU0Zc6+rKE0V2uMUw7hdTTl03JX4nj+TfRt63jtcZNFNBIQU0j+WxO/HpuMwUcR/4fXiWfg8n/0f+jhX7kX3oZcSAEFM5IdJj1+fb0xLjNVsyCi78HQN51VwPbhGiP/zU5J/LiC4lxyRJg63bN0MaNW99A+072bu6G3TkHLag5u3N/0CEef55D/paWwIPC95vjtlpJkiSpawy9JHXZjt1goRBw4gfYvK6eUPX2gVjb6EOJnHc2B35oMIf971Dy7v4VdR+bRNGfzqLu63mJYOzqOyiccQZ1FPHGZXfSMOtW3vn699ly+72s+NTXaXzwKVYfewp1z77GOgawtvhgNtbCplA/2uJRYkTYRD820a/b3mOYdvLZTAEdIVn7ZnJpIqepmWxayKGZ7A2JMeetjteeayab08mZ00wO4xKv3dFMDmeRfXM9OXyF7CtfI4fPkn3h00SZQoR2ot++lwgnEJpxN9kcR+jbvyKLIwmddQfZjCVCO5z7M3IYAef9mE0MJX7Rj2hiIBHaid3wcxooIHbn3bSQReSxROiW9Y8nk+8n+u/nAIjMm5cYX01sBQ0vXZqcE1pdnRjXb0i80NCQGFt2vVW0uwUd4nHTTeTv4+/RHXNaJx5N3k9vCTx8S5UQsCdrNXCUJElKL4ZekrrFjoHYduP5FxErLdsuEOsct0z/NlkhOOiwLPoxl7rDl1PEndRNPoaiB79L3dR+FD2b6Lqqu+YOimacwaaf3kHWN89ixXV3EL/gClZ97wbarr+VTfSl+n/OovWeP7Lm4/9L08NPse7ISlpeeIX6A8bQvLyGzeTT0KeMLQ3tbM7qy+bWLBrpQ1vHX4cxIjRQSAOFPf9DbO8YW7d5bXPH2Ngx1m5zbVnH+ELH+EBiiNzbRoQqorQR+VU7EW4g+os2IlxC5JZ2IpxN9MY2QnyNEHFCV8QJ8V+ELokT5pOEvhsnxCJCZ8cJsSAxZ0acEC91jC8QnhEnxL8JfRNCPJuY880QIf4F3wwR5mn4VogI/yBEHL4dJszf4NsRwjwOZ0YI8xiclUWYRwkTg5l5hHmI+PmFRHiQ+KUDifBHYpeXEuV+QsSJXzmIKPcSu3ooUf4fsWuHE+U3xK4/kCh3EyJO+40jyeZXtN88mix+Sfsth5HFHbTfOpYsfkGIOG0/H0c2P6P19nKy+Smtv5xANh0dZ3cdQQ4/oeXXR5HDLbTcM5FsbqH1t0eTzY8Tv0W/P5ZsfkTL748lh5tp+f2xZHMzrX84hmwS221b7zuGbH5Iy33HkMONtNx3DNncSMv9R5PDDxLf6/6jyeEGmu8/mlyup3nO0eRwPS1/nEgOibPrEh9fS/MfJ5LLNTRXHUjOY4MJEaf5zQJyHiukZVEBOX/pQ/OiAnL/UkDzonxy/lKamLM8n5xH82l5JY+cR/JofiWP3EdyaX4lj5xHBiTmvJ1HzsM5tLyaS87D2TS/mkvuw1k0L84l98/9AGhekUPuQxGaX8sh56EIzUtyyP1TUeLaymxy/xSiaUk2eX+CptezyX0Qmt/IIveBgsSc1VnkPhCj6Y0s8h5op2lpFrl/bKd5aZTcP+Ym5tREyZ3TStObUfLmtNC0LEru/S00L4uQe39WYs7aCLn3NdG0PELefVtoWh4m974tNL8VJvcP4cScdWFy/9BI01vbjiFCxGlaHyL39w00vb11zPt9PU3vhMi9N9F92bQhRO699TS/A7n31tHUMW7e0Eh0yaOEQtB6xJFEFy2ibezY5Jj18kLaxowj68X/JNbAEUcRfXkh7WPHEV20gLax5ckx64XnE1/nyKOILlpI29hxyTH7hechBK1HTiS6cAFt48qJLlxA+7ht7v/P1jlZi+bTNm480YWJsWDJk2wefShZz/87Meeoo5NfJ2vhfNrKxxNd0DE+/29CoTitRx1D1oL5tJaPJ3vhPFrLJ5C1oGP893OJOUcfQ9b8+bSOH0/2gnm0jp9A1nPPJb7H0ceQPb/jtY4xe8FLtI4/nKznnumYcxzZ81+idcLhZM3bYXy2Y84xx7372g5zWipPJt63b9f/blVaCoWgsBDq66PE964xOmWFQnt3vae+Rnd9n+6oY28FtZaKiqCubvt/endHLd31flKlllT5Gt31dVLlawAMGRJn0qT2HvlzlmpC8Xh6/Gdk3br6jPgPXigEAwYUZsz7Ue/Wnes5XFPd5a1trROPod/pn6HutkQI1jkC73qtq3PyZsyg+qY7ic68gM3kU3PxDwhffQNrzr6U6I9vpfYb3yH6iztpJoe6j51K6NHHaTjiA8RfXEDj8NHE3lrF5v2G0bZmIy1ks6VkMG0b6tnSt4zWTZtpKhxAa30zTfklxDY30U6E1tw+xJpaacvOJ9bSRltWHu2tMdoj2bS1h2gnQns4i/ZYiDayaCeceM3/v0KSJEnSbnjyyUbGjInt8f2plGV01tIV/stJUkrYVadYY8fY+dqOZ4u1jT40eej+zrrJkl1l//tl8u6+a6ffP4s2+ua1UkRiq9/AoRso4jnqDllFEY9SN+FUivg9AHWnVFD06M+oO+NwimZcT90Fd1A04xLqZt1B0YyzEnOu7gjUrusYb+gYf7hN+HZzx2s/7hhv6Rhv3WbOT7cP6Oqv+wF9LvguMcI0fH4q2b/7fzSeOJnok0+yZdyRhBcuYsvYw4ksepl2Imw+bAKRV16l6ZByQotfo2n0OMKvvcaWUWOJLnmNOCFaDj6U6Ouv0XLwIURfX0LzQYcQXfo6LSNGEn3zzcSc4SOIvrWc1v0PIPLO27QOGUZ45UpaBw0lsnoVcUK0lg0mUl1N64BSIuvW0jqwjMjaNcQI09p/PyLr19FaMoDIhg20FvcnUltLe24+oaYm4oSIESae6Bnb5a/unNdpZx+/3/Xe8HFQ3z9GiNC2dYRChOLxxO9bKEwoHiMWjhKKtRMPRyAWIx6OJD4nRDwSJdTett1Ie6KVMh7NItTWmhxjWdmEWluJZ2UTam1JzMnOgZYW4tk5hFqaieflE9qymVhhEaH6egDaB+5HZO0a2ssGE6leRdugIURWr6J98BAiq1Ym5gzZn/DKFbQP3Z/Iindo338YkXfepn3YcMJvv52YM/xAIm8to+2AEUSXv0nrgQcRXfYmAG0jRhJ98w3aDjqYyNLEGF36Om0jRxF9I3FeYOvBhxB9/TXaRh1CdMli2g45lMjixbQdchjRxa8kvs6hY4i++jKth44l+urLtB02lugri2gdU0705YWJOWPLiezQcRZZtIi2MeOSc1oPG0f0lUVb7z9sLNFXXk7cf9gYIq+8kvxebYeOIfLqK7QdehjRVxN1tB4yhujiV5K1tY4+jKzXXtm+1o6P32vc1Zy9vb8n5wRVR/uhhxLd5vdl29+fHV/ryrXdmRMbNJj2wUMIr1tHbMCAd481if/+xkrLkq9l1W6gtbhkl3O6dK0n53Th/lB1DYQ656wlNmDgdmN7/4GEa2o6vk7pdve39x9AeP06Yv0HEF7TMWe/0uRroXXrifXvT3h9x7hmTcec/TpeG0Bo/TriO9zf+Rrr1hPv35/Q+sQYXrMmUevA/QitX0+spD+hDeuJ7zDGSssgO3uX/xB+v38kv//1EKHQ3v1Le2+7XPb0/qysKK2tbYF9/0y5f28FXX+Q9w8ZEmPEiD0PvNKZoZektLOzgGzHYGyn4y62WXYlPNvxWqxkQE++bQDiffsRAiLECH1oEnm/+xWt//Vxip58gPA3v5AIyL71WYpmPAZA3ZmXUDTjQerO/i5FM+6j7pyzKJrxW+q+M52iGbMTc2ZOo2hGFXUzv0zRjF9Sd96XKJpxB3Xf/SJFM+5MzLng84nXLrojMV7SEcZduk1Ad1nHa1d2jFdsc+2qO5LnuBXNOCO5VbX+8h9QeMF3AdjyxS+R95tf0/zRk8l5/DFaDj+S7JdeSI7Au17b6zljy8letOA97j+C7JdepGX84WTPf4mW8vFkL5ifmNN535hxZL+8kJZDx5D96su0HHIo2YsT56a1jhpN1pLXaB05iqw3ltB60Eiylr5B64iDyHozcaZa6wEHkLV8Oa3DDiDr7eW0Dt2frBXv0DpkKFkrVwDQNmgw0dWr9tGKShPxnXwce48Rtm4V3nEEaNthbN1hBGjZYdzSMdZvM2dtx1jdMa7uGLf9rVrZMa7oGN/pGN/eZs5bHePyjnHZNtfe7BiX7jC+sc2c1zvGJR3j4h1GgFd3GF/pGF/eOiW+KPEQj84xtihEmDixV8OJ7cdAbEmUMG1bxzdyCNOcuPZmLmGaaF+eR4QttC/PJ8Jm2t8qINKxP7t9RSER6mlfVUSEOtpX9yXCJtqq+xFlIwBtNSVE2UDbmv5EWU/bmgFEWUdbbRnRjh92a9soslhCc5+J5PA8TSM+Se7ihwDYMupU8hbPYcuhnyVv8X1sGftf5C3+PVvK/5u8xb9NzDnyS+Qt/jWbj/4K+Yt/xebjvkb+4jvZ/IGvk7/4dgAaP/QtChb/lMYPn0XB4lto/Oh3KFh8M42Tz6Ng8Y0ANHziAvosvo6GT11Mn8VXU/+ZSym89grqPzuLwqsuA6D+81dTePnF1H/xOgq/fwH1U39A4YWJv/Pqpt1M0fnfoe6MWyg69yzqpv+UonO+Rd23fk7RmYmHqNSd3fF35nuMuz3nrJ2PwB5d2605q0n+NyPxfxa9ewSou3iHa7d1YU4XrvXknG79Hp3/vb1w5+NO51y0/QhQ9/3duHbx9uN2cy7e+bjlxC+z5StfS+lzGYM8Q7LkqcfZ8KGPBl7Hvp7jk7iVisJBFyBJPaUzLGsfW/6eY+MV19A465quXTtszE6Dsc0zvv2+wdrezOm8FkTo1t3iffslP249/oMANJ/6WQCavj59u3Fnr+31nG+duYv7ZyTG6d9MjDO+vXVO533fTnT2NZ11TmI8e2ZyzpbvnJcYz+0I9c5LPPVzy3cv2Drnexcnxgs7xosvTYyXXJacs/nSWQA0nnl24lrHzweg5ZjjEuPhR+503NW1PZ7TESLv9v0TDk+M4zvG8vHbfT2AljHjEuOhYxLjIR3j6EOTc1pHJZ7w2jpyVMc4MjEeNHLrnAMPSoxD90+Mww/Yem3/4dtfGzK0YxySnNM2aHBiLBtEbxHaYQx3pIvh2NY0MdzWtv3Y0rz1WlMTAJEtWzrGxIGEkc2NyTmRhkRqGKmr6xg3ARDd5qm20drEAzWiG9Z3jOsSY011ck5WR5dbzn+eByD3kYeS1/L+NCcx/vG+xHhfokM37/e/3TrnN78GIP/Xv0qMv0qE+/m/vD05p+AXP02MP7slMf7k5sT4oxuTc/rcmDh3r8/1iSf5Fl57RWK8auuf38LLE3+2C7+f+HPfGXgBFJ3/ncR4buLvkaJzvpUYz9z691Hnk4R3HPt88xtb6+h8yvCZMzrGxN9Zfc7+1tY5HU8gLugcZ57VMZ699T1/N1FPwbmJ1wrOP3frtY7/cyL/ws7x/MR40feSc/IvuaBjvDAxXv79xHjdVck5OR0/4+w/JH5fsh95GICsvzyanJP1t8cTY8dDWbKenpsY//XP5Jzoc88mxucTayDy4ovJa5GO/3MisijRoRh5JZHyRhZvTYLDryeS43BHV2X4rUT6HHpnRXJO8qnLHWsvtDaxFpMPfQHYmFjL1Hc8AGbz5q3XmjtS844/L8Qy+1yTvLvvIvraqxTceN1Ox/yf30r+bbfu8Zy9vb8n5+zsGrNmpUQd+3pO5JVF5N9wDZFFC941Flx6EQWXXbTb11JtTqrUsSe1hrf5b2lvYuglSXtoZyHarkKz7pqTvLaT0K0nw7fdCei6cn8mhHj7UvthYwFo/ejJydeavjwtMXZXCLjXQeEu7v9GR3i4Q4jY+fVgZyFix3jOLsLEczvCxPO2/uN7y/mJf3wnQ8QLLt567aJLtr/WETBuueTy5JzOoHHzZYkQo0cCxz0NE7s054jE2Bk8lk/oGMdvnTO2I3DsDB471ltn8AjQ2hE+to7oCBU7gsfExx3h44iO8YARHeOBW+cMOyAx7j8sMQ7tDByHJue0DUqEj52BY+cY6xPAw0VSwI5hZDKUjG8TRnZs3d0aSibaFsMtLVvndIaS7xFOAkQ6ntwb6QhuIvV1W69t2ghsDSijGxNPVekMKQGi6zuCyvXbB5XRt95KzkkGlX9PBFu5Dz0AQN6Dc5JzkkHlvYmgMu+3dyfGe2Yn5+Tf9cvEWJUI0QruuC15reC2xENJCn66Q2D54x8m5/S56frEeMO1ibEjmCu8ZlZyTuGVib8jCjvCu8JLE2Fe4SVb/64pujARDG4NMLeGiFtDzEQYWfStryfu7wgut/248JuJa51hZp9vbRNq7hhmnpX4un3O3vp/xHSGmn3eFWqetfXnct45ibEz1OwIOWFrwFnwvcTfrzuGnImPO4LOixPvP//7iY77/KuuSM7J6fjZZ9+TCJaz59yfGB/8Y3JO9p//lBh3CDyzHv9rck7Wk39LjE/9IzE+/XTyWvSZ/0uMPRF8dj7dumOraWhdR/C5YZunCu1O8Bn0IUg9oPNJ3OkQ0KVTmNhdtRp6SZLSyq4613oifNuj7rgUDvGYOTMlgsL3m2M42PN6JHDc0zCxS3M6uhY7g8cZ3+oYt+1e7AgcO4PHjqCvM3gE2NIRPnZ2K3YGj4mPOzsZO8bvXdgxXrR1TmdH40WJEGHLxZ2B47adjZcnxo7AMRk8XnLp1q/zxS8B0FxxArCPOxv3aajZhTByXMd9yVCyI4w8bJsw8tCOMLIzlNyhGzLx8cEdY2c42RFcdoSTiY8PSIyd3Y/Dhm+91tkZOXT7zsjWwdt0SCaDysTWpt4aVL6f0E4+DnWEIZ1h5s47LDvCzI6zB3fWafnujsstyTmRxkTXZTLU7Ag5YWvA2dl9uWPImfi4I+jc0NGNuS6xxzu68p3knJyOsCn3X4mQKvfxvyTGvzySnJP7cKIzc8fAM2/OH5Jz8n7/u8T4u98kxt/+Onkt2aHZE8HnFR2B5+WJ/7Ok8PsdwefF5yfn7Fbw+c0eCD7PSby2XeDZ0dGZDD47x90NPjs6O5PBZ+d46SXJOXk3Jn6uOT//GbB7wSdA1hMdnZ4pHnwChDr/fGzqCD47/0xt3vrnrrd1fKYqQy9JUkoIMsSLjSuHH/6QzVcEHxS+75zDxqRE+JYqIWBP1mrgGJydboX+wheBfdzZuE9DzS6Ekd/suC8ZSnaEkWeek5yz5ayOMLIzlNyhGzLx8fkd4/bbrDvDycTHHaFkZ/fjhVv/IZvsjLx4+87ILd+/PDlna1B5JbA1qOzslARo7giO36tTcmevBdchuTt1dISSHdu3t/04GWYmA8ydbOneIczcrsPykK6Emjvf7t0ZbsI23ZedweW22747As5ddmF2BJyd3ZixQkPNPbVPg8/mxGvbBZ6d2807g8/OcXeDz+T28x3GtTXJOVlLE0FSzvyXgN0LPgHy7u/ckp7awSds3a5edEFH8Pnd7besQ0AdnzsLPr+b+O9Dzm/uJuvJvxFdMK9XdX0ZekmSlEZipWWpEb6lSgjYk7X2UOC4112Ll12WVmGiW6EzU2dQ2dkpCduc2fgenZI7ey24DsndqaMjlOzYvr3tx8kwMxlg7mRL9w5h5nYdlmd3JdTc+Xbv7c+Q7Oi+7Awut9323RFw7rILsyPg7OzGbLy4F4WaPRF8HrZj8HlYck4y8OwcD95FN2dnx+aIbc65TG43H7H9nL0MPtsGdWw/NwDdbT0efHacqZn/qzvod/pnKK6sIHd2VTe9m9Rn6CVJktQFPRU47nXX4uWXExuXRmFiN2yFTpWOwlQNCoOudcv/fjnoP77qZr0q1OyJ4PPMHYPPrVsPk4Fn5zhzF92cnR2b393mnMvkdvMLt5+zl8Hn5ks7tp9fvJPt550BaMaEml2odULHlvVUDD47HhC0+evfZOO9f6T2ibk0TZ1GbxENugBJkiSpqzq3QgNs7viHQ+M2/4DofG3HMVXmpEodPVnr5vMvIlZa9r7hGSF22rW4qzldudaTc1Kljp6sdcv/fpm8u+9CvdOO28/zfvNrmk/9LDmPP0bT16eTPeOM5Ahsfe1bZ773tfcYA5vTlVq/MSMxTv/m1jkdHzfN+FZi/OaZ2309SASf2TPOoOnMszvGcxLj2d9JztlyzkyyZpyxdZx5XmI897tkdc459/zEa9+7ODF+93tbr511DlkzzqD5vz5PW0cA15sYekmSJEnaZ3YWVL5feBYKASd+gNi6+rQKE1OljnQINXtTmLg7AW46/8zcfq5UFIrH0+PZqevW1WfEU15DIRgwoDBj3o96N9ezMoVrWZnCtaxM4VpWpuhNazlcU03u7Cqap3yCnEf+vN2Y+/vfQQiaPvf53bqWanNSpY49qbVp6jRipWV7/PubSmu5s5YuzTX06lmptFCkveV6VqZwLStTuJaVKVzLyhSuZWWKVFrLuxN6eZC9JEmSJEmSMo6hlyRJkiRJkjKOoZckSZIkSZIyjqGXJEmSJEmSMo6hlyRJkiRJkjKOoZckSZIkSZIyjqGXJEmSJEmSMo6hlyRJkiRJkjKOoZckSZIkSZIyjqGXJEmSJEmSMo6hlyRJkiRJkjKOoZckSZIkSZIyjqGXJEmSJEmSMo6hlyRJkiRJkjKOoZckSZIkSZIyjqGXJEmSJEmSMo6hlyRJkiRJkjKOoZckSZIkSZIyzm6FXkuWLOG0005j4sSJXH/99cTj8fe959577+X4449nzJgxTJs2jTVr1uxxsZIkSZIkSVJXdDn0amlpYfr06YwZM4b777+fpUuXMmfOnF3e85///Icf//jH3HDDDfztb3+jubmZ66+/fq+LliRJkiRJknaly6HX3LlzaWho4MILL2TYsGHMnDmT++67b5f3LFu2jMsvv5xJkyZRVlbGqaeeyqJFi/a6aEmSJEmSJGlXol2duHjxYsaPH09eXh4Ao0ePZunSpbu853Of+9x2ny9btozhw4fvQZkQDkMXdlOmvFAoMWbK+1Hv5npWpnAtK1O4lpUpXMvKFK5lZYpUWsudtXRFl0OvhoYGhg4dus03CREOh9m0aRN9+/Z93/tra2u59957+cEPftD16rZRUlK4R/elqkx7P+rdXM/KFK5lZQrXsjKFa1mZwrWsTJFua7nL2xsjkQjZ2dnbvZaTk0NTU1OX7p81axaHH344J5xwwm4VKEmSJEmSJO2uLnd69e3bl9dff3271xobG8nKynrfe++77z7+85//8MADD+x2gZIkSZIkSdLu6nKn17hx45g/f37y8xUrVtDS0vK+WxsXLFjANddcw0033cSAAQP2vFJJkiRJkiSpi7ocek2cOJH6+vpkt9btt9/OpEmTiEQiNDQ00Nra+q571q1bx/Tp0znjjDMYM2YMjY2NNDY2dlvxkiRJkiRJ0s6E4vGun7v/xBNPcO6551JQUEB7ezv33HMPBx98MB/+8Ie56KKLqKys3G7+XXfdxbXXXvuur/Paa6/tfeWSJEmSJEnSe9it0AugpqaGhQsXcsQRR1BSUrKv6pIkSZIkSZL22G6HXpIkSZIkSVKq6/KZXpIkSZIkSVK6MPSSJEmSJElSxjH0kiRJSiO1tbW8+OKLbNiwIehSJEmSUpqhVw9asmQJp512GhMnTuT666/H49SU6mpra/nwhz/MihUrkq/tah27xpWqnnjiCT7ykY9w2GGH8bnPfY6lS5cCrmeln4cffpiTTjqJK664ghNPPJGHH34YcC0rvX31q19lzpw5gGtZ6efKK69k9OjRyV8f/ehHAdey0teNN97I9OnTk5+n+1o29OohLS0tTJ8+nTFjxnD//fezdOnS5H/cpVS0YcMGpk+fzsqVK5Ov7Wodu8aVqt5++20uuugizj33XObOncvgwYO5+OKLXc9KO3V1dVx55ZX85je/4YEHHmDWrFnceOONrmWltT/96U/885//BPzfGUpPL7/8MrfffjvPP/88zz//PH/84x9dy0pbS5Ys4be//S0XXXQRkCF/L8fVIx5//PH4xIkT45s3b47H4/H4q6++Gv/85z8fcFXSe5s6dWr8rrvuio8aNSr+zjvvxOPxXa9j17hS1d///vf4b3/72+TnzzzzTHzMmDGuZ6WdVatWxR988MHk56+++mr88MMPdy0rbdXW1sYnTZoUP/nkk+P/v737CWn6j+M4/ppmkUaKeFBQBFG+FLpKZofmYdboKNahKIIOHToIHvxDGmQLD+phsJMHD7NESgVhYHnJ1SHGgi6FB9dBPBQ6j6Uj53DrNhD57Ye/w7bP9/d83D7v0+fwZHx5s323tLREyzBOKpXKXLlyJbO3t3dkTsswUTqdzty9ezcTCASyMzu0zDe98iQWi+nSpUs6e/asJMmyrOzPa4BiNDY2pocPHx6Z5eqYxlGsurq6dO/evex5c3NTjY2N9Azj1NXVqbu7W5KUSqUUDAZ18+ZNWoaxJicn5fV6dfnyZUk8Z8A8379/VyaTUU9Pj5xOpx49eqStrS1ahpEWFxcVi8VUX1+vjx8/KpVK2aJlll55sre3p/r6+uzZ4XCopKREv379KuCtgH/W0NBwbJarYxqHCQ4ODhQMBnX//n16hrFisZjcbrcikYiePn1KyzDS58+fFY1GNTQ0lJ3RMkyzsbGhlpYW+f1+vXv3TmVlZRodHaVlGCeRSCgQCKixsVHxeFwzMzN68OCBLVpm6ZUnpaWlOn369JHZmTNntL+/X6AbASeXq2MahwkCgYDKy8t1584deoaxLMvSy5cv1dzcrJGREVqGcZLJpJ4/fy6fz6dz585l57QM03R3d2txcVFOp1MNDQ169uyZIpGI0uk0LcMo79+/158/f/Tq1Sv19vYqGAzq9+/fWlpaMr5lll55UllZeeyvxROJhMrKygp0I+DkcnVM4yh2kUhE8/Pz8vv9/9osPaOYORwOXbx4URMTEwqHw7QM40xNTam1tVUej+fInJZhuvPnzyudTqumpoaWYZR4PC6n06mqqipJ0qlTp2RZlpLJpPEts/TKk7a2Nn379i17/vnzpw4ODlRZWVnAWwEnk6tjGkcx+/HjhwYHB+Xz+dTc3CyJnmGeaDSqycnJ7Lm0tFSS1NTURMswyvLysj58+CCXyyWXy6W3b9/qxYsXCoVCtAyjjI+Pa2VlJXteW1tTSUmJLMuiZRiltrZWyWTyyGxra0tPnjwxvmWWXnnS0dGh3d1dhUIhSdL09LSuXbuWfWAFTJCrYxpHsdrf39fjx4/l9Xp148YNJRIJJRIJuVwueoZRmpqatLCwoIWFBW1vb8vv98vtdsvj8dAyjPL69WstLy8rFAopFArp+vXr6uvr09zcHC3DKBcuXFAgENCXL18UjUY1NjamW7duye120zKM4vF4tLGxoTdv3igej2t2dlbr6+vq7Ow0vmVHJpPJFPoS/xerq6saGBhQRUWFDg8PNTc3p5aWlkJfC8jJsiyFw+HsSwpzdUzjKEarq6vq7e09Ng+Hw4rFYvQMo3z69Enj4+OKx+Pq7OyUz+dTdXU1n80w2vDwsK5evarbt2/TMozj9/s1Pz+viooKeb1e9ff3q7y8nJZhnK9fv2piYkLr6+uqqanRyMiIvF6v8S2z9MqznZ0dra2tqb29XdXV1YW+DvCf5OqYxmEaeoZd0DLsgpZhF7QMuzC5ZZZeAAAAAAAAsB3e6QUAAAAAAADbYekFAAAAAAAA22HpBQAAAAAAANth6QUAAAAAAADbYekFAAAAAAAA22HpBQAAAAAAANth6QUAAAAAAADbYekFAAAAAAAA22HpBQAAAAAAANv5C6HQSD7tRFtZAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 96
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:35.349134Z",
     "start_time": "2024-09-19T11:39:35.284035Z"
    }
   },
   "cell_type": "code",
   "source": "train_data",
   "id": "f6fa1099e5163aeb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        age_range  gender  sell_sum  seller_id_unique  cat_id_unique  \\\n",
       "0             6.0     0.0       451               109             45   \n",
       "1             6.0     0.0       451               109             45   \n",
       "2             6.0     0.0       451               109             45   \n",
       "3             6.0     0.0       451               109             45   \n",
       "4             0.0     0.0        54                20             17   \n",
       "...           ...     ...       ...               ...            ...   \n",
       "257136        4.0     1.0       117                33             25   \n",
       "257137        0.0     1.0       198                38             20   \n",
       "257138        0.0     1.0       198                38             20   \n",
       "257139        0.0     1.0       198                38             20   \n",
       "257140        4.0     2.0       194                50             29   \n",
       "\n",
       "        brand_id_unique  item_id_unique  time_stamp_unique  \\\n",
       "0                   106             256                 47   \n",
       "1                   106             256                 47   \n",
       "2                   106             256                 47   \n",
       "3                   106             256                 47   \n",
       "4                    19              31                 16   \n",
       "...                 ...             ...                ...   \n",
       "257136               32              49                 12   \n",
       "257137               36              89                  6   \n",
       "257138               36              89                  6   \n",
       "257139               36              89                  6   \n",
       "257140               49             127                 23   \n",
       "\n",
       "        action_type_unique  time_stamp_std  ...  brand_id_most  \\\n",
       "0                        3      206.449449  ...         4094.0   \n",
       "1                        3      206.449449  ...         4094.0   \n",
       "2                        3      206.449449  ...         4094.0   \n",
       "3                        3      206.449449  ...         4094.0   \n",
       "4                        2      218.341897  ...         1236.0   \n",
       "...                    ...             ...  ...            ...   \n",
       "257136                   3       60.319805  ...         2276.0   \n",
       "257137                   3       55.674024  ...         6144.0   \n",
       "257138                   3       55.674024  ...         6144.0   \n",
       "257139                   3       55.674024  ...         6144.0   \n",
       "257140                   3      201.170042  ...         5696.0   \n",
       "\n",
       "        action_type_most  seller_id_most_cnt  cat_id_most_cnt  \\\n",
       "0                      0                  70               98   \n",
       "1                      0                  70               98   \n",
       "2                      0                  70               98   \n",
       "3                      0                  70               98   \n",
       "4                      0                  10                9   \n",
       "...                  ...                 ...              ...   \n",
       "257136                 0                  22               15   \n",
       "257137                 0                  28               38   \n",
       "257138                 0                  28               38   \n",
       "257139                 0                  28               38   \n",
       "257140                 0                  24               33   \n",
       "\n",
       "        brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                      70                   410              410.0   \n",
       "1                      70                   410              410.0   \n",
       "2                      70                   410              410.0   \n",
       "3                      70                   410              410.0   \n",
       "4                      10                    47               47.0   \n",
       "...                   ...                   ...                ...   \n",
       "257136                 25                   107              107.0   \n",
       "257137                 28                   162              162.0   \n",
       "257138                 28                   162              162.0   \n",
       "257139                 28                   162              162.0   \n",
       "257140                 24                   181              181.0   \n",
       "\n",
       "        action_type_sum_1  action_type_sum_2_x  action_type_sum_2_y  \n",
       "0                     0.0                 34.0                  7.0  \n",
       "1                     0.0                 34.0                  7.0  \n",
       "2                     0.0                 34.0                  7.0  \n",
       "3                     0.0                 34.0                  7.0  \n",
       "4                     0.0                  7.0                  0.0  \n",
       "...                   ...                  ...                  ...  \n",
       "257136                0.0                  9.0                  1.0  \n",
       "257137                0.0                  5.0                 31.0  \n",
       "257138                0.0                  5.0                 31.0  \n",
       "257139                0.0                  5.0                 31.0  \n",
       "257140                0.0                  9.0                  4.0  \n",
       "\n",
       "[257141 rows x 22 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>brand_id_unique</th>\n",
       "      <th>item_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>action_type_unique</th>\n",
       "      <th>time_stamp_std</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2_x</th>\n",
       "      <th>action_type_sum_2_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>256</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257136</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>60.319805</td>\n",
       "      <td>...</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>107</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257137</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>198</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "      <td>89</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>55.674024</td>\n",
       "      <td>...</td>\n",
       "      <td>6144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257138</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>198</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "      <td>89</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>55.674024</td>\n",
       "      <td>...</td>\n",
       "      <td>6144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257139</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>198</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "      <td>89</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>55.674024</td>\n",
       "      <td>...</td>\n",
       "      <td>6144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257140</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>194</td>\n",
       "      <td>50</td>\n",
       "      <td>29</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>201.170042</td>\n",
       "      <td>...</td>\n",
       "      <td>5696.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>181</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>257141 rows × 22 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 97
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:38.038163Z",
     "start_time": "2024-09-19T11:39:38.005983Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# xgb调参\n",
    "features_columns = [col for col in train_data.columns if col not in ['user_id','label']]\n",
    "train_data_1 = train_data[features_columns]\n",
    "test_data_1 = test_data[features_columns]\n",
    "target =target"
   ],
   "id": "aa63f735f15eab59",
   "outputs": [],
   "execution_count": 98
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:39.846714Z",
     "start_time": "2024-09-19T11:39:39.829409Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def model(train_1,target_1):\n",
    "    X_train, X_val, y_train, y_val = train_test_split(train_1, target_1, test_size = 0.2 ,random_state = 42)\n",
    "\n",
    "    clf = XGBClassifier()\n",
    "\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    y_train_pred = clf.predict_proba(X_train)\n",
    "    y_train_pred_pos = y_train_pred[:,1]\n",
    "\n",
    "    y_val_pred = clf.predict_proba(X_val)\n",
    "    y_val_pred_pos = y_val_pred[:,1]\n",
    "\n",
    "    auc_train = roc_auc_score(y_train, y_train_pred_pos)\n",
    "    auc_test = roc_auc_score(y_val, y_val_pred_pos)\n",
    "\n",
    "    print(f\"Train AUC Score {auc_train}\")\n",
    "    print(f\"Test AUC Score {auc_test}\")\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_val, y_val_pred_pos)\n",
    "    return fpr,tpr,clf,auc_test"
   ],
   "id": "cb36866174d2a1ae",
   "outputs": [],
   "execution_count": 99
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T03:27:56.966636Z",
     "start_time": "2024-09-19T03:27:56.009118Z"
    }
   },
   "cell_type": "code",
   "source": "fpr_1,tpr_1,clf_1,auc_test_1=model(train_data_1,target)#0.6153997",
   "id": "bc26f58c49bdc3e1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.8099121275136587\n",
      "Test AUC Score 0.5795435413715768\n"
     ]
    }
   ],
   "execution_count": 108
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:49.335586Z",
     "start_time": "2024-09-19T11:39:49.319912Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_auc_curve(fpr, tpr, auc):\n",
    "    plt.figure(figsize = (16,6))\n",
    "    plt.plot(fpr,tpr,'b+',linestyle = '-')\n",
    "    plt.fill_between(fpr, tpr, alpha = 0.5)\n",
    "    plt.ylabel('True Postive Rate')\n",
    "    plt.xlabel('False Postive Rate')\n",
    "    plt.title(f'ROC Curve Having AUC = {auc}')"
   ],
   "id": "8a06feff9633aad",
   "outputs": [],
   "execution_count": 102
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:52.144521Z",
     "start_time": "2024-09-19T11:39:52.128522Z"
    }
   },
   "cell_type": "code",
   "source": "plot_auc_curve(fpr_1, tpr_1, auc_test_1)",
   "id": "8fd473024f1e065e",
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fpr_1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_20168\\3005364828.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mplot_auc_curve\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfpr_1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtpr_1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mauc_test_1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m: name 'fpr_1' is not defined"
     ]
    }
   ],
   "execution_count": 103
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:39:54.004250Z",
     "start_time": "2024-09-19T11:39:53.999239Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_learning_cuve(model, X, Y,num):\n",
    "    \n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 11)\n",
    "    train_loss, test_loss = [], []\n",
    "    \n",
    "    for m in range(num,len(x_train),num):\n",
    "        \n",
    "        model.fit(x_train.iloc[:m,:], y_train[:m])\n",
    "        y_train_prob_pred = model.predict_proba(x_train.iloc[:m,:])\n",
    "        train_loss.append(log_loss(y_train[:m], y_train_prob_pred))\n",
    "        \n",
    "        y_test_prob_pred = model.predict_proba(x_test)\n",
    "        test_loss.append(log_loss(y_test, y_test_prob_pred))\n",
    "        \n",
    "    plt.figure(figsize = (15,8))\n",
    "    plt.plot(train_loss, 'r-+', label = 'Training Loss')\n",
    "    plt.plot(test_loss, 'b-', label = 'Test Loss')\n",
    "    plt.xlabel('Number Of Batches')\n",
    "    plt.ylabel('Log-Loss')\n",
    "    plt.legend(loc = 'best')\n",
    "    plt.show()"
   ],
   "id": "161ef58179472b0",
   "outputs": [],
   "execution_count": 104
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T11:40:14.148220Z",
     "start_time": "2024-09-19T11:39:59.541125Z"
    }
   },
   "cell_type": "code",
   "source": "plot_learning_cuve(XGBClassifier(), train_data_1, target,5000) ",
   "id": "a4286789f3e9886f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 105
  },
  {
   "metadata": {
    "jupyter": {
     "is_executing": true
    },
    "ExecuteTime": {
     "start_time": "2024-09-19T12:16:39.458316Z"
    }
   },
   "cell_type": "code",
   "source": [
    "smote = SMOTE(random_state = 402)\n",
    "X_smote, Y_smote = smote.fit_resample(train_data_1,target)\n",
    "sns.countplot(Y_smote, edgecolor = 'black')"
   ],
   "id": "eabe44d02a941935",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function _draw_all_if_interactive at 0x0000024CD1995550> (for post_execute):\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\pyplot.py\u001B[0m in \u001B[0;36m_draw_all_if_interactive\u001B[1;34m()\u001B[0m\n\u001B[0;32m    118\u001B[0m \u001B[1;32mdef\u001B[0m \u001B[0m_draw_all_if_interactive\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    119\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[0mmatplotlib\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mis_interactive\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 120\u001B[1;33m         \u001B[0mdraw_all\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    121\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    122\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\_pylab_helpers.py\u001B[0m in \u001B[0;36mdraw_all\u001B[1;34m(cls, force)\u001B[0m\n\u001B[0;32m    130\u001B[0m         \u001B[1;32mfor\u001B[0m \u001B[0mmanager\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mcls\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_all_fig_managers\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    131\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mforce\u001B[0m \u001B[1;32mor\u001B[0m \u001B[0mmanager\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcanvas\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfigure\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstale\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 132\u001B[1;33m                 \u001B[0mmanager\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcanvas\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdraw_idle\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    133\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    134\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
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      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001B[0m in \u001B[0;36mdraw\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    398\u001B[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001B[0;32m    399\u001B[0m               else nullcontext()):\n\u001B[1;32m--> 400\u001B[1;33m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfigure\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdraw\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrenderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    401\u001B[0m             \u001B[1;31m# A GUI class may be need to update a window using this draw, so\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    402\u001B[0m             \u001B[1;31m# don't forget to call the superclass.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001B[0m in \u001B[0;36mdraw_wrapper\u001B[1;34m(artist, renderer, *args, **kwargs)\u001B[0m\n\u001B[0;32m     93\u001B[0m     \u001B[1;33m@\u001B[0m\u001B[0mwraps\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdraw\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     94\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mdraw_wrapper\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0martist\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mrenderer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 95\u001B[1;33m         \u001B[0mresult\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdraw\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0martist\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mrenderer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     96\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mrenderer\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_rasterizing\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     97\u001B[0m             \u001B[0mrenderer\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstop_rasterizing\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001B[0m in \u001B[0;36mdraw_wrapper\u001B[1;34m(artist, renderer)\u001B[0m\n\u001B[0;32m     70\u001B[0m                 \u001B[0mrenderer\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstart_filter\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     71\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 72\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mdraw\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0martist\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mrenderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     73\u001B[0m         \u001B[1;32mfinally\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     74\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0martist\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_agg_filter\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\figure.py\u001B[0m in \u001B[0;36mdraw\u001B[1;34m(self, renderer)\u001B[0m\n\u001B[0;32m   3173\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3174\u001B[0m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpatch\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdraw\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mrenderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 3175\u001B[1;33m             mimage._draw_list_compositing_images(\n\u001B[0m\u001B[0;32m   3176\u001B[0m                 renderer, self, artists, self.suppressComposite)\n\u001B[0;32m   3177\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\image.py\u001B[0m in \u001B[0;36m_draw_list_compositing_images\u001B[1;34m(renderer, parent, artists, suppress_composite)\u001B[0m\n\u001B[0;32m    129\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[0mnot_composite\u001B[0m \u001B[1;32mor\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mhas_images\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    130\u001B[0m         \u001B[1;32mfor\u001B[0m \u001B[0ma\u001B[0m \u001B[1;32min\u001B[0m \u001B[0martists\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 131\u001B[1;33m             \u001B[0ma\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdraw\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mrenderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    132\u001B[0m     \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    133\u001B[0m         \u001B[1;31m# Composite any adjacent images together\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001B[0m in \u001B[0;36mdraw_wrapper\u001B[1;34m(artist, renderer)\u001B[0m\n\u001B[0;32m     70\u001B[0m                 \u001B[0mrenderer\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstart_filter\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     71\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 72\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mdraw\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0martist\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mrenderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     73\u001B[0m         \u001B[1;32mfinally\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     74\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0martist\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_agg_filter\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001B[0m in \u001B[0;36mdraw\u001B[1;34m(self, renderer)\u001B[0m\n\u001B[0;32m   3026\u001B[0m                 \u001B[0martists\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mremove\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mspine\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3027\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 3028\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_update_title_position\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mrenderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   3029\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3030\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0maxison\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001B[0m in \u001B[0;36m_update_title_position\u001B[1;34m(self, renderer)\u001B[0m\n\u001B[0;32m   2961\u001B[0m                 if (ax.xaxis.get_ticks_position() in ['top', 'unknown']\n\u001B[0;32m   2962\u001B[0m                         or ax.xaxis.get_label_position() == 'top'):\n\u001B[1;32m-> 2963\u001B[1;33m                     \u001B[0mbb\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0max\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mxaxis\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_tightbbox\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mrenderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   2964\u001B[0m                 \u001B[1;32mif\u001B[0m \u001B[0mbb\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   2965\u001B[0m                     \u001B[1;32mif\u001B[0m \u001B[1;34m'outline'\u001B[0m \u001B[1;32min\u001B[0m \u001B[0max\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mspines\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001B[0m in \u001B[0;36mget_tightbbox\u001B[1;34m(self, renderer, for_layout_only)\u001B[0m\n\u001B[0;32m   1326\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1327\u001B[0m         \u001B[1;31m# go back to just this axis's tick labels\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1328\u001B[1;33m         \u001B[0mtlb1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtlb2\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_get_ticklabel_bboxes\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mticks_to_draw\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mrenderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1329\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1330\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_update_offset_text_position\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtlb1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtlb2\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001B[0m in \u001B[0;36m_get_ticklabel_bboxes\u001B[1;34m(self, ticks, renderer)\u001B[0m\n\u001B[0;32m   1302\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mrenderer\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1303\u001B[0m             \u001B[0mrenderer\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfigure\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_get_renderer\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1304\u001B[1;33m         return ([tick.label1.get_window_extent(renderer)\n\u001B[0m\u001B[0;32m   1305\u001B[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001B[0;32m   1306\u001B[0m                 [tick.label2.get_window_extent(renderer)\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001B[0m in \u001B[0;36m<listcomp>\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m   1302\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mrenderer\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1303\u001B[0m             \u001B[0mrenderer\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfigure\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_get_renderer\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1304\u001B[1;33m         return ([tick.label1.get_window_extent(renderer)\n\u001B[0m\u001B[0;32m   1305\u001B[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001B[0;32m   1306\u001B[0m                 [tick.label2.get_window_extent(renderer)\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\text.py\u001B[0m in \u001B[0;36mget_window_extent\u001B[1;34m(self, renderer, dpi)\u001B[0m\n\u001B[0;32m    957\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    958\u001B[0m         \u001B[1;32mwith\u001B[0m \u001B[0mcbook\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_setattr_cm\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfigure\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdpi\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mdpi\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 959\u001B[1;33m             \u001B[0mbbox\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minfo\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdescent\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_get_layout\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_renderer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    960\u001B[0m             \u001B[0mx\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_unitless_position\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    961\u001B[0m             \u001B[0mx\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_transform\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtransform\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\text.py\u001B[0m in \u001B[0;36m_get_layout\u001B[1;34m(self, renderer)\u001B[0m\n\u001B[0;32m    384\u001B[0m             \u001B[0mclean_line\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mismath\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_preprocess_math\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mline\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    385\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mclean_line\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 386\u001B[1;33m                 w, h, d = _get_text_metrics_with_cache(\n\u001B[0m\u001B[0;32m    387\u001B[0m                     \u001B[0mrenderer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mclean_line\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_fontproperties\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    388\u001B[0m                     ismath=ismath, dpi=self.figure.dpi)\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\text.py\u001B[0m in \u001B[0;36m_get_text_metrics_with_cache\u001B[1;34m(renderer, text, fontprop, ismath, dpi)\u001B[0m\n\u001B[0;32m     95\u001B[0m     \u001B[1;31m# Cached based on a copy of fontprop so that later in-place mutations of\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     96\u001B[0m     \u001B[1;31m# the passed-in argument do not mess up the cache.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 97\u001B[1;33m     return _get_text_metrics_with_cache_impl(\n\u001B[0m\u001B[0;32m     98\u001B[0m         weakref.ref(renderer), text, fontprop.copy(), ismath, dpi)\n\u001B[0;32m     99\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\text.py\u001B[0m in \u001B[0;36m_get_text_metrics_with_cache_impl\u001B[1;34m(renderer_ref, text, fontprop, ismath, dpi)\u001B[0m\n\u001B[0;32m    103\u001B[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001B[0;32m    104\u001B[0m     \u001B[1;31m# dpi is unused, but participates in cache invalidation (via the renderer).\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 105\u001B[1;33m     \u001B[1;32mreturn\u001B[0m \u001B[0mrenderer_ref\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_text_width_height_descent\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtext\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mfontprop\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mismath\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    106\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    107\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001B[0m in \u001B[0;36mget_text_width_height_descent\u001B[1;34m(self, s, prop, ismath)\u001B[0m\n\u001B[0;32m    232\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    233\u001B[0m         \u001B[0mfont\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_prepare_font\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mprop\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 234\u001B[1;33m         \u001B[0mfont\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mset_text\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ms\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m0.0\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mflags\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mget_hinting_flag\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    235\u001B[0m         \u001B[0mw\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mh\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfont\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_width_height\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m  \u001B[1;31m# width and height of unrotated string\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    236\u001B[0m         \u001B[0md\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfont\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_descent\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "fpr_2,tpr_2,clf_2,auc_test_2=model(X_smote,Y_smote)#0.6022299",
   "id": "659ebfece69b96f8"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "plot_learning_cuve(XGBClassifier(),X_smote, Y_smote,9600)",
   "id": "9e55bba8c43b763a"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "smote = SMOTE(random_state = 446)\n",
    "X_smote1, Y_smote1 = smote.fit_resample(train_data_1,target)\n",
    "\n",
    "\n",
    "X_final = pd.concat([X_smote, X_smote1], axis = 0).reset_index(drop = True)\n",
    "Y_final = pd.concat([Y_smote, Y_smote1], axis = 0).reset_index(drop = True)\n",
    "\n",
    "sns.countplot(Y_final, edgecolor = 'black')"
   ],
   "id": "f6aabf6f6d21f313"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "fpr_5,tpr_5,clf_5,auc_test_5=model(X_final,Y_final)#0.6027413",
   "id": "d4ea0ce563baf514"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:01:25.768801Z",
     "start_time": "2024-09-19T12:01:25.759206Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 特征优化\n",
    "def feature_selection(train, train_sel, target):\n",
    "    clf = XGBClassifier()\n",
    "    \n",
    "    scores = cross_val_score(clf, train, target, cv=5)\n",
    "    \n",
    "    scores_sel = cross_val_score(clf, train_sel, target, cv=5)\n",
    "    \n",
    "    print(\"No Select Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))     \n",
    "    print(\"Features Select Accuracy: %0.2f (+/- %0.2f)\" % (scores_sel.mean(), scores_sel.std() * 2))\n",
    "    return scores.mean(),scores_sel.mean()"
   ],
   "id": "34165e92650ef594",
   "outputs": [],
   "execution_count": 107
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:01:31.061349Z",
     "start_time": "2024-09-19T12:01:31.051851Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def select(train,goal):\n",
    "    a_score=[]\n",
    "    b_score=[]\n",
    "    for i in range(2,train.shape[1]+1):\n",
    "        sel = SelectKBest(mutual_info_classif, k=i)\n",
    "        sel = sel.fit(train, goal)\n",
    "        train_sel = sel.transform(train)\n",
    "        \n",
    "        print('训练数据未特征筛选维度', train.shape)\n",
    "        print('训练数据特征筛选维度后', train_sel.shape)\n",
    "        mean_train,mean_test=feature_selection(train, train_sel, goal)\n",
    "        a_score.append(mean_train)\n",
    "        b_score.append(mean_test)\n",
    "    x=list(range(2,train.shape[1]+1))\n",
    "    plt.plot(x, a_score, marker='o', markersize=3)  # 绘制折线图，添加数据点，设置点的大小\n",
    "    plt.plot(x, b_score, marker='o', markersize=3)\n",
    "    plt.xticks(x)\n",
    "    plt.show()\n",
    "    return a_score,b_score"
   ],
   "id": "3cf845b995846acc",
   "outputs": [],
   "execution_count": 108
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:10:16.144551Z",
     "start_time": "2024-09-19T12:01:36.001809Z"
    }
   },
   "cell_type": "code",
   "source": "a_score,b_score=select(train_data_1,target)",
   "id": "7cf049da0f6ed02c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 2)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 3)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 4)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 5)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 6)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 7)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 8)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 9)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 10)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 11)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 12)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 13)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 14)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 15)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 16)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 17)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 18)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 19)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 20)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 21)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 22)\n",
      "No Select Accuracy: 0.94 (+/- 0.00)\n",
      "Features Select Accuracy: 0.94 (+/- 0.00)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 109
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:55.512501Z",
     "start_time": "2024-09-19T12:12:55.507422Z"
    }
   },
   "cell_type": "code",
   "source": "x=list(range(2,train_data_1.shape[1]+1))",
   "id": "61742a59d0949644",
   "outputs": [],
   "execution_count": 110
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:13:01.096385Z",
     "start_time": "2024-09-19T12:13:00.923626Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.plot(x, a_score, marker='o', markersize=3)  # 绘制折线图，添加数据点，设置点的大小\n",
    "plt.plot(x, b_score, marker='o', markersize=3)\n",
    "plt.xticks(x)\n",
    "plt.show()"
   ],
   "id": "77fe393eea2a71a4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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UKVO47777MBqNHD58mDfffLPec4YOHcrSpUt55plnWLhwIdnZ2WzZsoXi4mLeffdd4uPjycnJ4fHHH2fRokWtbueq0tJKmpi73SyKUvuLdndcV2PHxHQjpOosFYUHsIakuC1uS3kqtuTs/7ElZ+/Elpy9E9sfc/ZkbG/k3BSXC5+YmBiOHj1a75jJZCIkJKTBNklJSWzZsoVjx46xcOFCEhISyMjIqJ+ATkdGRgZPPfUUWVlZZGdnk5+fz8SJE0lJqf2CnjlzJgMHDqSiooLo6OhWtXOVquL2X4on4zYV2xabQsgPe9GeP9bs8/sq57YY15Ox/TFnT8aWnL0TW3L2Tmx/zNmTsT2Zc1NcntyclpbGV1995fy5qKgIq9XaYG9PHUVRMBgM7Ny5k+nTpzuPb9q0ieXLlzt/1mq1zvk/druds2fPOh87ffq083hL2wU75y3tZTLBWQghRPByucdn0KBBVFZWkpeXx+23386SJUvIzMxEq9ViNBrR6/UN9v4sXryYcePGkZqa6jyWkpLCrFmz6Ny5M3369GHRokWMHTsWgPT0dFasWEHHjh0JCwtj5cqVDBgwgLi4uBa3C3bOPbvKZfVmIYQQwcvlwken0zFv3jymT5/OggULsNvtrF69GoDbbruN7OxsRo0adVm7EydOsHnzZjZv3lzveGpqKnPmzGH+/PlUVlYyduxYZsyYAcCkSZM4ffo0ixcvpqysjAEDBvDss8+2ql2ws1+6erOqemZKvRBCCNHGKWpTyy//RElJCQcOHCA9PZ34+HhP5eVzZ8+6f0JXQkKU2+O6HNtuIeHvPVBUB2cf2Ica2b7t59yG4noytj/m7MnYkrN3YkvO3ontjzl7MrY3cm6Kyz0+dTp06ECHDh1alJTwIa0eR1RntBUn0JUfo8aFwkcIIYQIND7fpFR4jz22GyATnIUQQgQvKXyCiK1ugrPs2SWEECJISeETRC5OcJY7u4QQQgQnKXyCyMXCR4a6hBBCBCcpfIKIcy2fihPgsPk4GyGEEML7pPAJIg5DR1RdGIrDhrbipK/TEUIIIbxOCp9gomiwx1y4s0vm+QghhAhCUvgEGbmzSwghRDCTwifIyARnIYQQwUwKnyBjj7tkzy4hhBAiyEjhE2TsMdLjI4QQInhJ4RNknENdphKwmnycjRBCCOFdUvgEGTUsFkd4OwB05XJnlxBCiOAihU8QkgnOQgghgpUUPkHIFisTnIUQQgQnKXyCkLPHp0x6fIQQQgQXKXyCkLPwkTk+QgghgowUPkHIfunqzarq42yEEEII75HCJwjZY5JRFQ0aayVK1RlfpyOEEEJ4jRQ+wUirxxHVGQBduUxwFkIIETyk8AlS9ti6Xdql8BFCCBE8pPAJUja5s0sIIUQQksInSF2c4Cx3dgkhhAgeUvgEKVm9WQghRDCSwidIOXt8Kk6Aw+bjbIQQQgjvkMInSDkMHVF1YSgOG5qK732djhBCCOEVUvgEK0WDPab2zi6d3NklhBAiSEjhE8Rsl67gLIQQQgQBKXyCmExwFkIIEWyk8Ali9ri6wkd6fIQQQgQHKXyCmD1GCh8hhBDBRQqfIOYc6jL9CFaTj7MRQgghPM/nhU9JSQn79u3DaDR6pZ24SA2LxRHeDgBduazgLIQQIvA1q/A5cuQIEyZMYNCgQeTk5KCqaqPPV1WVpUuXMmbMGIYMGcLcuXOpqqpyPr58+XJuueUWZs+ezfDhw9mzZ4/zsWXLlpGZmUl6ejqPPPIIZWVlLrWrM3XqVObNm9eclxeUZIKzEEKIYOJy4WO1WsnKyiI1NZX169dTUFDAhg0bGm2Tm5vLqlWreOGFF1izZg379+9n9uzZABQWFvL666+Tn5/Ppk2bmDx5Mq+++ioAe/fuJS8vj9WrV7Nx40YsFgs5OTlNtqvzySefsHv3bv70pz81680IRjaZ5yOEECKIuFz47NixA6PRyIwZM+jSpQvTpk0jNze30TZ5eXk8+OCD9OvXj5SUFB599FG2bdsGgN1uZ968ebRv3x6A3r17U15eDsD+/fsZNmwYKSkpJCcnM378eI4fP95kOwCz2czcuXOZPn060dHRrr8TQUru7BJCCBFMdK4+8dChQ/Tv35/w8HAAevXqRUFB48MjZWVlJCYmOn/WaDRotVoAunfvTvfu3QEwmUysXr2aMWPGANCjRw/Wrl3LvffeS2RkJLm5uWRmZjbZDmDx4sWYzWZ0Oh27du3iuuuuQ1EUV1+mUwuauBTP3XFbG9sRe7Hw+Wn7tpqzL+J6MrY/5uzJ2JKzd2JLzt6J7Y85ezK2N3JuisuFj9FoJCkp6ZITKGg0GsrLy4mJiblimz59+rB161ZGjx4NwIYNGxg6dGi952zfvp3HHnuMpKQksrKyABg2bBjJycnOdmlpaTz00ENNtisuLmbFihX069eP4uJi/vGPf9CpUydee+21Zhc/7dpFNev5vo7b4tiOfgCElBeS0M5wxU9Om8vZh3E9Gdsfc/ZkbMnZO7ElZ+/E9secPRnbkzk3xeXCR6vVEhoaWu+YXq/HbDY3WPhMnTqVKVOmcN9992E0Gjl8+DBvvvlmvecMHTqUpUuX8swzz7Bw4UKys7PZsmULxcXFvPvuu8THx5OTk8Pjjz/OokWLGm23YcMGEhISWLFiBaGhodx///2MHDmSTz/9lOuvv7457wulpZU0MXe7WRSl9hft7ritjq0m0A4FxVJB6cljqJHt237OPojrydj+mLMnY0vO3oktOXsntj/m7MnY3si5KS4XPjExMRw9erTeMZPJREhISINtkpKS2LJlC8eOHWPhwoUkJCSQkZFRPwGdjoyMDJ566imysrLIzs4mPz+fiRMnkpJSOwwzc+ZMBg4cSEVFhXPezpXalZSUcN111zkLNIPBQHJyMkVFRa6+TCdVxe2/FE/GbXFsjR5HdGe0FSfRnj9GTUT7y57S5nL2YVxPxvbHnD0ZW3L2TmzJ2Tux/TFnT8b2ZM5NcXlyc1paGl999ZXz56KiIqxWa4O9PXUURcFgMLBz506mT5/uPL5p0yaWL1/u/Fmr1Trn/9jtds6ePet87PTp087jjbXr2LEjFovF+ZjD4eDHH3+sN89IXM4eKxOchRBCBAeXe3wGDRpEZWUleXl53H777SxZsoTMzEy0Wi1GoxG9Xt9g78/ixYsZN24cqampzmMpKSnMmjWLzp0706dPHxYtWsTYsWMBSE9PZ8WKFXTs2JGwsDBWrlzJgAEDiIuLa7TdTTfdxIQJE3jvvffo378/q1atwmq1kp6e3pr3KODZYlMIPfkx2jJZy0cENo2xGCpL0CgdsEe67w8iT8UVQrify4WPTqdj3rx5TJ8+nQULFmC321m9ejUAt912G9nZ2YwaNeqydidOnGDz5s1s3ry53vHU1FTmzJnD/PnzqaysZOzYscyYMQOASZMmcfr0aRYvXkxZWRkDBgzg2WefbbJdSkoKL730Ei+//DLHjh2jS5cuLF68GIPB0LJ3J0jYY68BQHteVm8WgSvs4BoMHz8JqoM4RYNxRA7mvhPbbFwhhGcoalPLL/9ESUkJBw4cID09nfj4eE/l5XNnz7p/QldCQpTb47ojdsj3nxD7r4nYYq+h7Nfb3Ra3MZ6KLTn7f2xPxNUYi4n/x3UoqsN5TEXBfM0tEBLR8sA1VYQVbOLSeyFVRcu5+3fhMLin58ef3mdPx5ac/T+2N3Juiss9PnU6dOhAhw4dWpSUaJucc3wqToDDBppmfyyEaNO05wvrFT0ACirhBZvcfi5FtaMtP+62wkcI4V7yDSdwGDqh6sJQbGY0Fd/jiO3m65SEcCt7bDdUqN8zg0LVgN+j6lu+wrtiqSDiP39D4eKfrqqixR7TtcUxhRCeJYWPAEWDPaYbutJv0Z0/hlUKHxFgFKsJBZzFj6poMY543i1zcRyx3TB8/CSK6kAFjNfPld4eIdqwZu3OLgKXzTnBWW5pF4FHf+z/AKi5+ucwaTNlk3a5bQKyue9Eyu7fDdGJtUVVaKRb4gohPEMKHwFcupaP3NIuAk/osXcBsPS8A7rd4PYeGUdUIgycDEDY4fVujS2EcC8pfAQgixiKwKWpKCLkzH5URYO125imG7RUv18BEFL0ae26PkKINkkKHwFI4SMCl77wwjBXp0GoEQmeO1FcMjWJQ1BQ0R/J89x5hBCtIoWPAC4pfEw/gtXk42yEcJ+6YS5ryk0eP5e51wTgwnCXrzYiEkI0SgofAYAaFosjvB0AunJZwVkEBqXqLCE/7AXA0m2cx89n7T4eVatHd+4wurPfePx8Qojmk8JHOMkEZxFo9MffR1Ed1FzVD0d0ksfPp+pjsHQdXXtumeQsRJskhY9wssXIPB8RWEIL6oa5PN/bU8dSN9x1JK92JXQhRJsihY9wssdJ4SMCh2KpILToUwAsXpjfU8faZQSOsHg01WcI/X6H184rhHCNFD7CSe7sEoEk9MSHKA4rtrju2ON7eO/E2hDMPX4JyHCXEG2RFD7CyX7p6s1yR4rwc6EXVmu2emFS80/VDXfpC99DsRq9fn4hRMOk8BFO9phkVBQ01kqUqjO+TkeIlrNVoz/xIQCWa7w3zOU8ffv+2GKvQbGZCS3Y4vXzCyEaJoWPuEirxxHdGQBduQx3Cf8V+v0nKLYq7IZEbFf1834CioKl110AhB3O9f75hRANksJH1CPzfEQgqNuU1JIyDhTFJzmYe94BQMipXWgqZQsLIdoKKXxEPba6wqdM1vIRfsphI7TwfcA7qzU3mEZ0EtbE6y5sYbHBZ3kIIeqTwkfUc3GCs6zeLPxTSPFnaCzncYTFU9NpsE9zca7pc3iD3DAgRBshhY+oR1ZvFv5Of6x2MrGl2xjQaH2ai+WaC1tYlB1Bd+aAT3MRQtSSwkfU4yx8Kk7IqrPC/6iOi7ex+3CYq46qj8bSbSwga/oI0VZI4SPqcRg6oerCUBw2NBXf+zodIZpFV/IlWlMJjhAD1qShvk4HuGS46+g78seEEG2AFD6iPkWDPaYbIHd2Cf+jP3Zhb66uvwBdmI+zqWXtPAxHeDs01WcJPbnd1+kIEfSk8BGXkVvahV9SVULrCh8frNbcIG0I5h63AzLcJURbIIWPuIzt0q0r/IzGWAyFO2r/VwQV7bnD6MqPo2r1WJNv9HU69dTbwsJS4eNshAhuUviIy9j9dC2fsINriFt5Hay8lbiV1xF2cI2vUxJe5Bzm6nwDaqjBx9nUZ7sqDVtcDxS7Bb1sYSGET0nhIy7jj0NdGmMxho+fRFEdACiqA8NHT6IpP+njzIS3hDpXa/b93VyXURTMdb0+R2S4SwhfksJHXMYee2Fys+lHsPjHztLa84XOoqeOgoO4t28ictdzflXEiebTlJ8g5Ow3qIoWa9fRvk7niiw970BFIfTULjQVRb5OR4igJYWPuIwaFocjLL72h3P+Mdxlj+3GT9fFVQGNtZyIfYuJf3MYMRvvQn94A9iqfZGi8KC6vblqEoeghsf7OJsrc0RdTc3VPwcg7MhGH2cjRPCSwkdckT2udoIzpd/5NhEXKVYTcHEzSlXRYhz+POU3LcWSPBJV0RBavJvorY/S7o0MInc8jfbsQd8lLNxKX9iGh7kuUW+4S7awEMIndL5OQLRNtpgUQn7YC2e/g45jfJ1OkyL2voSCiqXLcPQ3Pk6Z0h57ZCJQu4KvprKYsEPrCDu4Fq3xFBEHVhBxYAU17a/F3Hcilh6/bHMTYoVrFNNpdD98DoA1ZayPs2mc9ZqbUXfMRFf2Hboz+7G17+/rlIQIOtLjI67IHlc7wdkfeny0pd8S9t2/AKjKnAndbsBhSKz3HEdUIlWDpnLutzs5f+vq2j2UNCGEnP6SqI+fpN2KdAwf/hndj1/IX+J+Rl/4PgoqNe2vvez33taooVEXt7A4lOvjbIQITlL4iCuqu7OL0qO+TcQFkXteBMB8zS3YE/o2/mSNlpouI6gY93dKJ+3FmPkUtthrUGxVhH+7lrj1vyRu7SjCv1qGYi7zQvaitepuY7dc07aHueo4t7D47l9gr/FxNkIEH58XPiUlJezbtw+jsXl3D7W0nXCNPeZC4XPmMJrKtrsYoO7M1+iPvYuKQtXgac1qq0YkUD0gi7L7Pub8Hesx97qrdiftc4cx/HsO7d7IIOr9hwkp+hRUhyyO2AYp5vOEnPoUaBubkrqidguLq9BUlxL6vWxhIYS3NavwOXLkCBMmTGDQoEHk5OSgNjEkoKoqS5cuZcyYMQwZMoS5c+dSVVXlfHz58uXccsstzJ49m+HDh7Nnzx7nY8uWLSMzM5P09HQeeeQRysrKXGpXp6amhltvvZXPPvusOS9RXBDyw57au6Rqqoj7R9tdDDBiz/8AYOnxS+zxPVsWRFGoSRxC5aiXKf2vfVQOe5aahFQUu4Wwo+8Q+849tFt+LXFvDJHFEduY0BPbUBw2bPG9LvZStnUaHeaetwOyhYUQvuBy4WO1WsnKyiI1NZX169dTUFDAhg0bGm2Tm5vLqlWreOGFF1izZg379+9n9uzZABQWFvL666+Tn5/Ppk2bmDx5Mq+++ioAe/fuJS8vj9WrV7Nx40YsFgs5OTlNtrvUsmXLOHLkiMtvhLhIYyzGsGOm8x4pRXVg+Pgvba6nQ1fyH/THP0BVNM3u7WmIqo/BnDaJ8/e8R9ndW6hO/S0OXQQa8zmUCzfMt9X3Ixg5h7lS2tDeXC64uIXF+yiWch9nI0Rwcbnw2bFjB0ajkRkzZtClSxemTZtGbm7jk/Py8vJ48MEH6devHykpKTz66KNs27YNALvdzrx582jfvj0AvXv3pry89h+A/fv3M2zYMFJSUkhOTmb8+PEcP368yXZ1jh8/zvLly7n66qtdfXniEldcDFC1oy0/7puEGhBZ19vTa4JH/tq3te+HccR8Ksb972WPtcX3I+jUVBN68mPAf4a56tgSUrHF97qwhUW+r9MRIqi4XPgcOnSI/v37Ex4eDkCvXr0oKGh8cbuysjISEy/eZaHRaNBqtQB0796dkSNHAmAymVi9ejVjxtTeNt2jRw8++OADTp48SWlpKbm5uWRmZjbZrs6sWbOYMmWKFD4tZI/thqrU/2ioKNhjuvomoSvQ/fA5oSc/RlW0mDIe8+i57O16X+H9AHt0skfPKxoX+v3HKDYz9qjO2BJSfZ1O8ygK5l53AjLcJYS3uVz4GI1GkpKSnD8rioJGo7mst+VSffr0YevWrc6fN2zYwNChQ+s9Z/v27Vx//fWcPXuWrKwsAIYNG0ZycjKjR48mMzOT6upqHnrooSbbAaxfvx6j0cjkyZNdfWlXpCju/89Tcd0dW41KxHhjDqqirfeeaKtOt5mcI/e8AIClz69QY5M9+j7/9P1QqV0qMfzAijbxXgRibFfiOjclTRmHolH8IudL/7P2urCFRfFnaCu/94uc20Jsydn/Y3s656YoalMzlC9YuHAhNpuNGTNmOI8NHz6ct99+mw4dOlyxTVFREVOmTCEuLg6j0cjhw4d58803ycjIcD7HZrPx5Zdf8swzzzB48GCys7PZsmULr732Gq+99hrx8fHk5ORgNBpZtGhRo+3OnTvHrbfeyrJly+jTpw+//e1v+eMf/8iQIUNcezdEfeWn4Nwx+Ox/4dBm6PAzeOhj0Ib4Nq/j/4Y3xoMmBB7dB7FdvHPeuvej5Bv4vydrj936Kgyc5J3zi4tsVnihO5jL4b/+D5J/7uuMWmblbVC4HW58CoY/7utshAgKLq/cHBMTw9Gj9dd0MZlMhIQ0/CWYlJTEli1bOHbsGAsXLiQhIaFe0QOg0+nIyMjgqaeeIisri+zsbPLz85k4cSIpKbXzNmbOnMnAgQOpqKggOjq6wXbPPvssd911F3369HH5DWhIaWmlW9exUxRo1y7K7XE9GVtRomnX7QbOhXQm9vinaEq+xrT1BaoH/tENsVuYs6oS8/5fCQGq+07EZIuDs5Wtj+tSzrXvR2n0tYSX/kjE3pdQ86dRoWlPTefrWxHX/z4bnoztStyQk9uJMZfjCE/gXHifep+BtprzlehTbieqcDu2/7zF+b7/fcU/Wdtazr6MLTn7f2xv5NwUl4e60tLS+Oqrr5w/FxUVYbVaiYmJaSIRBYPBwM6dO5k+fbrz+KZNm1i+fLnzZ61W65z/Y7fbOXv2rPOx06dPO4831m7z5s2sWrWKjIwMMjIy+OKLL8jKymLJkiWuvkwnVXX/f56K68nYAI6wdhiH1t6NF7HnJTRlx3yWs+77fxNS/BmqVk/VwD/67H02DZqGuccvURw2ot59CM2579rk789fYzcVN7Tgwt5c3caiKlq/yPlK/1lSbkLVhaE7fwxtyZd+kbOvY0vO/h/b0zk3xeUen0GDBlFZWUleXh633347S5YsITMzE61Wi9FoRK/XN9j7s3jxYsaNG0dq6sUJiCkpKcyaNYvOnTvTp08fFi1axNixtUu5p6ens2LFCjp27EhYWBgrV65kwIABxMXFNdqu7o6xOtOmTWPSpEnccMMNrr5M0QBLrwlYj2wg9PsdGD7+C+W/XOf6gKq7qCqRny0EoDr1177dnkBRqBz5P2grTxHy4+fEbJ5E2V2b2uzO4AHFYXfuxu4vqzU3RA01YEm5ibAjGwk7vB5jhwG+TkmIgOdyj49Op2PevHnMnj2bzMxM3nvvPWcPzm233cb27duv2O7EiRNs3ryZqVOn1juemprKnDlzmD9/PnfccQeJiYnO+UOTJk1i/PjxLF68mKeffpqoqCgWLFjQZLukpKR6/+n1ehISEpzDY6IVFIXK4fNRdWGEntpJ2LfrvJ5C6MmPCCnZh6oLoyq99cNtraYLo/zm17FHd0FbcYKYd38Hdouvswp4upJ9aKrP4AiNpubqTF+n02rOHduPvgN2q4+zESLwNWt39lGjRvH+++9z4MAB0tPTiY+v/ev2ww8/bLBNcnIyX3zxxRUf++Uvf8kvf/nLy47r9XqeeuopnnrqqWa1+6lVq1Y1+RzhOkdMMqbBf8aw8xkid87D0vUXqBFXeefkqupcpbn6Z5NQI9t757xNUMPbUT7+DWLX/5KQH/YQ9eHjVI56xfu9YUGkrrfH2vUXoA31cTatV5N0PfaI9mirThN68mOs3cY03UgI0WLN3qurQ4cOjBo1yln0iOBS3f931FyVhsZSjuGT2V47b+jxDwg5/RWqLoKq9D947byusMf3pGLc31EVLWFHNhDx+Su+TilwqarfrtbcII0OS4/bAQiTNX2E8Difb1Iq/IxGh/HGBbVf8t/9i9DjW5tu01qqg8jPatftqe43GTW8nefP2Uw1nYdhHPYsULvGkP7oOz7OKDBpS79FW3ESVavH2uVGX6fjNubedwEQenwrivm8b5MRIsBJ4SOazXZVGtX9fweAYfsMFKvRo+cLPfYuutKDOEIMVA34b4+eqzXMP/sNVf1rF9qM2jYN3Y9XHuIVLacv2AKAtcsICInwbTJuZE/oi61d7wtbWGz2dTpCBDQpfESLmAb/GXt0MlrjD0TszvHciRx2Ive8CNQOs6lhcZ47lxuYMmdi6ToGxW4hZstkNBUnfZ1SQLk4zOXfd3Ndibln7STnsMONb/4shGgdKXxEy4SEUzliPgDhB97wWO+GvmAzunOHcehjqL52ikfO4VYaLRWjF1GTkIqmupSYzQ+gWCp8nVVA0J4/hu7cYVSNrnZic4Cx9LwdFYWQH/ZIwSyEB0nhI1qspvMwzL3uQkEl6qMn3H8rrsNGRF1vz7UPoepj3BvfU0IjqRi/AntkB3RlR4h+7/fgsPk6K78XeuFurpqrM9t8z19LOAydqOlcu+aYN3p9NMZiKNxR+79C3o8gIoWPaBXj0Fk4wuLRnTtMxH/+162x9Ufy0J0vwKGPpbpf6zad9TaHIZGK8W+g6sIJ/X47hh1Pu76sqLgi56KFgXI31xVc3LE916Ofl7CDa4hbeR2svJW4ldcRdnCNx87lD+T9CC5S+IhWUcPjMV4/B4CIz19BW1bgnsD2GiL3vgRAVfrvUUOb3n+lrbFdlUbF6EWoKIR/s4rw/a/7OiW/pTH+ULt4JQrWbmN9nY7HWLrdhKoLR1d+HF3JPo+cQ2MsxvDxkyiqAwBFdWD4+C9B29Mh70fwkcJHtJql5x1YuwxHsVswfPwEXPgHpDXCDueirTiBI7wd1Wn/5YYsfcOaMg5TZu1CnJH/nkto4Qc+zsg/hRa+B4CtYzqOyA4+zsaDQiOdE7c9MdylPXsQw46nnV/ydRTVjrb8uNvP5w+05wvl/QgyUviI1lMUKoc/XzusU/xZ67uJ7VbnIoBV6Q/7/W3L1dc+RHXf+1BQiX7/YbRnvvF1Sn7n4jBX4N3N9VN1a/q4awsLxWok7Js3if3neOLXjUF/oYi8lKposcd0bfW5/JH27MHLjgXz+xEMpPARbuGI7oxpyBMARO58Fo2ppMWxwr5dh7ayCHtEe6p/9lt3peg7ioJx2LNYk65HsVURkz8JjelHX2flNxRzGSGndgGBPb+nTs3VQ7FHdkBjOU/oiYa3A2qUqqL78QsMH06n3Yp0oj5+snblc00IlmvGU9V/CqpS+8+/Chh/PtO3m/76SEjxbgy7nqt3TFU0GEc8H5TvR7CQwke4TXW/ydS074/GWoHhk1ktC2IzE/HFqwBUDfwj6MLdmKEPaUOoGPd3bHHd0Zp+JDp/MtRU+TorvxB6fCuKasfWrg+OYPgrXKNt8RYWirmM8K+WEbd2FHHrf0n4t+tQbFXYYq/BmPk0pZP2UjHu75iun03ZpN2Q0AsFUIJwbznt+WNEb/kdiqMGc/dbqUnoC4Bx+HOY+070cXbCk6TwEe6j0VJ540JURYu+IJ/QY5d3qTcl7OBbaI0/YDd0wtz3Pg8k6TuqPoby8W/gCIsn5Mx+oj94xC3zoQKdviBwFy1sSN2O7aHHtzW9hYXqIKToU6Lef5h2KwZi+Pec2vWOdGGYe99N2R0bKLvvY6oH/DdqRIKzmcOQCENqVxrXB9keYYq5jOj8B9BYzlPTYQCVv3gRe3xPADRWWXcr0EnhI9zKntCX6gvbShh2zESxVrre2FZNxBevAVA18FHQhXkiRZ9yxHSl/ObXUTWh6AvfI3LXfF+n1LZZTYR+vx0IjmGuOrVbWPRFcVjRf3flLSw0phLCv3iN+NU3EPvOPYQdfQfFYaUm4WdUDn+O0ge+oPIXL2FLHAwN9eik3omqCSHk7NdoSw958BW1IXYr0f/3ELrzx7BHJVF+83LQheOITgZAUy6LRwY6KXyE25kGTa3dzsL0I5G7nne5XfjXq9BWncYelYS5zz0ezNC3bJ0GUTmydtPViP/8jbAvFsvCaQ0IPfkRit2CPToZe7s+vk7Hq+p6ffTfvHXx8+GwEXp8K9H5k4lfORjD7udr734MjaI69beU/epdzt/zf5h/dr9rC35GxGPtOhIIkp3hVRXDxzMIPbULR4iB8vFvoEZcBYA9prbw0cqq2QFP5+sERADShVN544Lav0K//gfmnndg65TReJuaKiL2/T8AqjL+BNpQLyTqO5Zed2IqLyRy70tE7noOdkGcosE4IkfmF1zCuTfXNTc13GsRoCw9bydy5zOEnNlfu7AeCmpoVL2hmJpOg6juex+Wa8a3+O5HS68J6I+9h/7IRkzX/QU0Wne9hDYn/D+LCT+0DlXRUDH2b9jb9XY+Zo/uAoC2/ISv0hNeIj0+wiNqkoZS3fueS7azsDT6/PADK9BUl2KPTsbc6y4vZelb5j4TUYG6r3NZOO0n7BbnXU3BNL/HSbVTe89VLQUVjbUChz6Gqv4PcW7iR5y/cyOW3ne3askHa9df4NDHoDX96Lx7LhCFfrcZw4WhZeMNf6Um+cZ6jzsu9PhoKotki5kAJ4WP8BjT0KdwhCegKztCxL7FDT5PsVYSse9vtW0GTQVtiLdS9ClteSE/7cOQhdMuCin6FI21EntEB2wdBvg6Ha/Tnr/88wFQMWYxputnYY/v4aYT6bF0vw2oXTg0EOlKviR6658AqOo3GXPaA5c9xxHZAbR6FNUuf3wEOCl8hMeoYXEYb5gLQMTni9CeO3rF54XvX47Gch5b7DVYet7hzRR9yh7bzbmWSh1V0cjCaRfU3c1lTRkLSvD9U3Xlz4fWfQXPJZzziQq2BNwyC5rKU0RvmYxit2BJHolp6OwrP1HRQNyFeT4y3BXQgu9fE+FVlu63YUkeieKwEnWF7SwUSznhXy4BoGrwtICeX/BTDkMixhE5qMrF12zpNlYWTgNw2AktfB8I0mEuLv98qIrWYwvr2ToOxB6djGKrcq6SHQgUq5GY/Eloq05ja9ebyjGLG/83Jq4bANoKKXwCmRQ+wrMUBePw+ai6CEJ+2EvYN2/Wezj8y6VoLOXY4nth6X6rj5L0HXPfiZRN2gVDpwIQcvormV8AcHI3mupSHPoYahKv83U2PuP8fEzaTNmkXZ6b+K4ozp3hw44EyN1dDhtR7/8BXekh7BHtKR+/EjXU0HibuK6A3NkV6KTwER7niLoa03VPAhC56zk0xh9qH6g6R9iXywAwDZ4WlMMZcGEhuRF/wREWj9ZY7NyQM6gdql27xtp1dNDM+WqIw5AI3W7weE9g3XBXyPeftGrLmbYi8tO/oj/xIaoujIqbl+OIurrpRnWFjwx1BbTg/KYRXled9gA1HQagsVZi+OTp2oM7F6GpMWJr1xdrkA5nOIWEYU79NQDh+1f4OBkfU1X4dhMQvMNcvuCI6UpNxwwU1YH+SJ6v02mVsANvELF/OQAVo17B1uFa1xrG1w51aaTHJ6BJ4SO8Q6Ol8sYFqBod+mP/R/jn/w921d7pZRry56Dt7bmUOe23qIqW0OLdaEu/9XU6PqM78RGUf4+qDcPaZZiv0wkqdb0+/ryYYciJj5x7BRqv+wvWa8a73liGuoKCfNsIr7G360PVgN8DELl7PtjNqICmqtS3ibURDkMi1gvbMoTvf8O3yfhI2ME1xGyeVPuD3UyYn/c8+BtL91tQNaHoSg+iPXvQ1+k0m7b0W6Lf+z2K6qC69z1Upz/cvACxF9bysZSjmMs8kKFoC6TwEV5l7v2rS5Zkq128z7BdFu2rU93vvwAIO7Kh6c0pA4zGWIzh4ydRLnxCFJAFHb1MDYvD2vUXgP/1+iim08RsfgBNjRHr1T/HOGJ+81f7Do3AHtEBkF6fQCaFj/AqrbFYFu1rRE2nIdja9UaxVRP27Tpfp+NV2vOFKD9d7kA+G17nXNPnaB447L5NxlW2amK2TEZrPIUtphsV45a0eNsbR4xsXRHopPARXtXgomyyaF8tRaE6rbbXJ/zrf/jPF48bhHz/78uOyWfD+6zJI3HoY9GaSgg59amv02ma6iBq61RCTn+JQx9LxS0rUcPiWhyubs8umeAcuKTwEV7lzUXZ/JW55521eydVnCD05Ee+TscrQo+9S8S+1wBQL/QJymfDR7ShWHrUbWHR9oe7Ij57gbCCzaiaECpuXoY9NqVV8S7u0i49PoFKdmcXXmfuO5Ga5OHEq6cpU9pjj5QvtnpCwjH3uZeIL/9O+P4VWLuO8nVGHqU7vZ/oDx5BQaU6bRLV6Q8Tj3w2fMncawLhX/8DfcEWKoc9B6GRvk7pivSH/knkF68CUHnjArcsdumQXdoDnvT4CJ/w1qJs/qo6bRIqCqHfb0dbVuDrdDxGU1lMdP5/odjMWLuMwHj9XBxR8tnwNVuHdGwx3VBs1egL3/V1OlcUcmoXUR89AYBp4CO1u9S7Qd3QqkxuDlxS+AjRBjmiuzh7esIPBOiChlYTMfkPoK0qwRbfi4qxfwONdEK3CYqCpW4Li8MbfJzM5TTnjxH97hQURw3ma26hasjjbovtnONjLAa71W1xRdshhY8QbVTdre36Q/9EsVb6OBs3c9iJ/uBhdKUHcYQnXNhHKcrXWYlLmHvWFj4hRf++uM2Mj2mMxfBtPjHv/BqN5Tw17a+lctRLbl0AVY24ClUXjqI60FYWuS2uaDuk8BGijapJugFbXHc0NSb0h3J9nY5bRe6ch/74VlStnvKbX8cRneTrlMRPOGKSqek0uM1sYRF2cA1xK6+Ddfehrfwehz6W8puXgy7cvSdSFLmzK8D5vPApKSlh3759GI1Gr7QTwm8oCtVpDwAQfuAN+MkaN/4q7Ot/EPFV7ea0lb94GVvHgT7OSDSkrezY7lzc8pJrQLFWoKg2j5zPHl13Z5cUPoGoWYXPkSNHmDBhAoMGDSInJwdVVRt9vqqqLF26lDFjxjBkyBDmzp1LVVWV8/Hly5dzyy23MHv2bIYPH86ePXucjy1btozMzEzS09N55JFHKCsrc6ndunXruP7660lNTWXy5MmcPn26OS9RiDbF0usuHCEGdOcLCPn+E1+n02ohJz/GsKN2k1rTkCew9LjVxxmJxliuqdvC4pBPt7C48uKWDo8tbmmXRQwDmsuFj9VqJSsri9TUVNavX09BQQEbNjQ+6S03N5dVq1bxwgsvsGbNGvbv38/s2bMBKCws5PXXXyc/P59NmzYxefJkXn219rbEvXv3kpeXx+rVq9m4cSMWi4WcnJwm233++ee88sorLFiwgG3bttVrJ4Q/UkMNmC/crRJ+4A3fJtNK2tLDF/ZRsmPudRdVAx/xdUqiCWpYLNZutZPsfbmmjz3qan76Z7YnF7e82OMjhU8gcrnw2bFjB0ajkRkzZtClSxemTZtGbm7j8w7y8vJ48MEH6devHykpKTz66KNs27YNALvdzrx582jfvj0AvXv3pry8HID9+/czbNgwUlJSSE5OZvz48Rw/frzJdoWFhcyZM4fMzEw6duzInXfeyddff928d0SINsZ8YZJz6PGtaPz0L1Cl6iwx+Q+gsVZi7TSEyhtzmr+PkvAJc6+7ANAf2QgOzwwtNSXsUC4KOIsfTy9ueXEtHxnqCkQu3zt66NAh+vfvT3h47USyXr16UVDQ+PoiZWVlJCZe/GBqNBq02toVe7t370737t0BMJlMrF69mjFjxgDQo0cP1q5dy7333ktkZCS5ublkZmY22e7uu+uv41BYWEhycrKrL7Eed/+bXBfPE//Weyq25Oyd2E3FdcSlYO0ynNCT2wn/+h9UXf+022K3hsux6/ZRqvwee0xXKm9ehqLTtz5uC8jnrvlxa5JH4AiLQ1t1mtCif1OTPMJtsV2hLT1MxL7/B4BpxHwMyWmc19QubunOt+TSnB11u7RXnKjdNLcVL8QfPxuejO2NnJvicuFjNBpJSrp454WiKGg0GsrLy4mJiblimz59+rB161ZGjx4NwIYNGxg6dGi952zfvp3HHnuMpKQksrKyABg2bBjJycnOdmlpaTz00ENNtrtUWVkZ69atY+HCha6+xHratfPMrbWeiuvJ2JKzd2I3Gvf6h+Gt7UQcWkvEzbObvZKuz94PVYX1f4KSfRAWg/a3ubRL6NL6uK0kn7tmxk27C/YuJeb4v2Cg6/OyWp2zwwF5M8BRAz1vwjD896AotHwnrqa1axcF0X0ABU2NiYQIK0QmuCeuh/hjbE/m3BSXCx+tVktoaP3dbvV6PWazucHCZ+rUqUyZMoX77rsPo9HI4cOHefPNN+s9Z+jQoSxdupRnnnmGhQsXkp2dzZYtWyguLubdd98lPj6enJwcHn/8cRYtWtRou0vNnTuXAQMGMGLECFdfYj2lpZU0MXe7WRSl9hft7riejC05eye2S3HjriMuOhltxQkqd/4Dy89+49OcXY0d8dkLRHy9HlWjo2LcEmroCGcbX5PI1zm3pbiejN2cuLrkW4nduxT1202cK/4BNdTglZzD9r+BoWgPjhAD5zPnop4zeu19jjN0RGv8gfOF32DrmO62uO7kj7G9kXNTXJ7jExMTw7lz5+odM5lMhISENNgmKSmJLVu2MG/ePBITExk6dCgZGRn1nqPT6cjIyOCpp55yTpbOz89n4sSJpKSkEBsby8yZM3n//fepqKhotF2d3NxcPv/8c5577jlXX95lVNX9/3kqridjS85tJGc0VKdNAmonOasO1ec5NxU79NB6Iva+DIBx+PNYrx7a5nNui3HbQs417Qdgi01BsVUTUvCuV3JWKouJ2PU8AKaf/wV7ZKJX32fnWj7lJ9rs789fY3s656a4XPikpaXx1VdfOX8uKirCarU22NtTR1EUDAYDO3fuZPr06c7jmzZtYvny5c6ftVqtc/6P3W7n7Nmzzsfqbkm32+2NtoPaidHPPfccL774IgkJre+eFKKtMPe5B1UXjq70ECHFu32dTqN0xXuI+rB2G4Gq9D9g7nuvjzMSraIoWHpNALx0d5eqYtg+E02NkZqOAzH/7H7Pn/Mn7NFdAVnLJxC5XPgMGjSIyspK8vLyAFiyZAmZmZlotVqMRiM1NTUNtl28eDHjxo0jNTXVeSwlJYVFixbxwQcfUFRUxKJFixg7diwA6enpvP3226xZs4aNGzcybdo0BgwYQFxcXKPtzp49S1ZWFlOmTCE1NRWTyYTJZGrJ+yJEm6PqYzBf+PJpy/t3acqPE/PugygOK5aUmzBd9xdfpyTc4OIWFp/Wbh3hQaEF+eiPf4CqCaFyxAK3bknhKoes5ROwXP406XQ65s2bx+zZs8nMzOS9995z9uDcdtttbN++/YrtTpw4webNm5k6dWq946mpqcyZM4f58+dzxx13kJiYyIwZMwCYNGkS48ePZ/HixTz99NNERUWxYMGCJttt3ryZ0tJSXn75ZdLT053/CREo6lZyDj32HppKz375tIRiPk/M5klozGXUtO9PxahXffKlJdzPEd0Za6chKKge3cJCMZ8n6sIil1Xpf8DerpfHztWYi9tWSOETaBS1qeWXf6KkpIQDBw6Qnp5OfHy8p/LyubNn3T+hKyEhyu1xPRlbcvZO7ObGjcn7FaGndlKV/kdMP2+8N8Wr74e9hphNvyH01KfYDZ04f9dmHJEdWh/Xkzm38biejN2SuGEH3yLqoyewxfei7N6tDd4/3JqcDR89TvjBNdjiulN2z3ugvbj0gTffZ92P+4hbfxt2QyfOTdrrtrju5I+xvZFzU5r9p1iHDh0YNWpUQBc9QrRldb0+YQffApvZt8nUUVUMO7IJPfUpjpBIysevbFHRI9o2yzXjUbV6dOcOozv7jdvjh5zaRfjBNQC1Q1zahtd78jR7TDIAGuOPbec6E24hfdBC+BlrtzHYDYlozOfQf7fJ1+kAEP7l3wk/uAZV0VA5ZjH2hL6+Tkl4gKqPwdK1dn01vbsnOdvMGD56AoDq1N9gSxzs3vjNpIbF4wgxoKCirSzyaS7CvaTwEcLfaHRUX7jLJXz/Ctfv4fSQ0GP/R+TOZwEwDZ2FtesvfJqP8Czn3V1H8ty6hUXE56+gKy/EHtEB08+zm27gaYpyydYVMs8nkEjhI4QfMve9D1WrJ+TMfnQl+3yXSPF/iHr/ERRUqn82iep+D/ouF+EV1i4jcITFo6k+Q+j3O9wSU3v2IBH/+RsAxmHzUPXRbonbWnW7tMsE58AihY8QfkgNj8fc43bgQq+PD2hL/gP/uAPFVo21y3CMN8yVjUeDgTYEc49fAm4a7nLYifroCRSHDUvKOKzX3Nz6mG5ycZd2WcsnkEjhI4SfMvd7AAB9QT6K6bRXzx22/w1i/3krmMtQAUvyKNC4vAOO8HN1w136wvdQrMZWxQo/8AYhp7/EERqFcdgz7kjPbeomOMsu7YFFCh8h/JTtqjRqOmagOGoI/2a1186rK96D4ZOnnDtjK4Dh33M8vqidaDts7ftji70GxWYmtGBLi+NoKk8RuTsHANPPs3FEdnRXim5Rt5aPVoa6AooUPkL4sep+/wVA2DerwW71+PlCj28jZvNv+emAlqLa0ZYf9/j5RRtRbwuL3JbFUFUM22eg2Kqo6TQYc+qv3Zige1wc6jrh85sIhPtI4SOEH7Ok3Iw9ogPaqtPoW/GXd5NUBxF7XiQ6/wE0NSZ++hWgKlrsMV09d37R5ji3sDi1q0WriOu/+xf6Ex+iakKpHJHTJlf4dkRdjapoUGxmlKozvk5HuEnb+6QJIVynDcH8s98AtXMlPEExnyc6/wEi977ovHvLOHw+qlK7ObCqaDGOeB6HIdEj5xdtkyM6CWvidRe2sNjQrLaKuQzDJ7MBqBr4R+zxPTyRYutpQ52faxnuChwyG1EIP1fd99dEfP4qIT9+ju7MAWxXpbkttvbsQWLenYK24gSqVk/liBwsve8CoKbbL4hXT1OmtMceKUVPMLL0mkBo8W7CDq+nOv1hl+/qi/z0GTTVZ7HF9aRq4MMezrJ17NHJaCuL0FacwNZpkK/TEW4gPT5C+Dk1sj2Wa8YD7r21XX94PXHrb0NbcQJ7dBfOT3jHWfQAtX8Jd7tBenqCmHMLi7Kj6M4ccKlNSNGnhB9ah4pC5Y2+3ZbCFXbnLu1yZ1egkMJHiABQN8lZf/QdlOrS1gWzWzHseIrorX9CsZmxdhlB2d352K76mRsyFYFE1Udj6TYWcHFNH1s1URe2pTD/7H5snTI8mZ5b1JvgLAKCFD5CBABbh3Rq2vdHsVsIu7DJY0toTD8Sm/cr53whU8afKB+/EjUszk2ZikDjvLvr6Dtgr2n0uZF7X6rtQYzsiOnnf/FGeq3mkEUMA44UPkIEAkWhOq221yf863+0aA+lkOLdxK27iZAfP8cRGk35+DeoGvI4aLTuzlYEEGvnYTjC26GpPtvoFhbaM98Q/p+/A2Ac9ixqaJS3UmwV57YVsl9XwJDCR4gAYel+C46weLTGYkIL33e9oaoS/tUyYvLuQVN9Blu73pTdnY+16yjPJSsChytbWDjsRH38BIpqx3LNeKwpY72YYOs4h7qqTkNNtY+zEe4ghY8QgUIXRvWFReDCD7g4ybmmiqgP/ojh33NQVDvmHrdTNuFfOGK7eTBREWgsvWonvesL30OxVFz2ePj+5YSc/gpHaDTGG/7q7fRaRQ2LxaGPAWS4K1BI4SNEADH/7LeoipbQU7vQln7b6HO1548Rl3srYUffQdXoqLzhr1SOXgQhEV7KVgQK21Vp2OJ6oNgtly2kqan4nsjPFgBgypyJI7KDL1JsFdm6IrBI4SNEAHEYErGmjAMg/MDKBp8XWvg+sf8cj+7cYewR7Tn/y7cx95ssu6uLllEUzHUbl166hYWqErX9Lyi2aqyJQzD3neijBFtHdmkPLFL4CBFgnPt3HV6PYj5f/0GHnYjdC4jZMhmNtZKaToM5/6t3sSUO9n6iIqBYet4BQGjxbjQVRQDoj2wk9OR2VK0e44gFbXJbClc4ZIJzQPHPT6EQokE1nYZga9cbxVaN/tu3nccVcxkxm+8n8otXAajqN5nzv1znl0MPou1xRF2N9epMoLbgwVRK5CdzAKjK+BP2uGt8mF3ryFBXYJEtK4QINBdubY/6+EnCv3odUgaiKysnans22soiVF0YlTcudP6FLoS7mHtNIPTUTvTfroMze9GYz2GL70XVgCxfp9Yq9uiugAx1BQrp8REiAJl73olDF47WeApW/ZKYzfejrSzCHp1M2YR/SdEjPMJ6zc2oig5d+XEo+BAVsHQdDdpQX6fWKs5tKyq+B9Xh42xEa0nhI0QA0ljKUGxm588KoALl45djT+jrs7xEYFOslaBeXDxTASL+8zc0xmLfJeUGDkMiqkaHYregMf3o63REK0nhI0QA0p4vREGtd0wBNK3dx0uIRtR+7upTVDva8uO+SMd9NDochqsBGe4KBFL4CBGA7LHdUH9yB42qaLHHdPVNQiIoBPLnzh5Te0u7RnZp93tS+AgRgByGRIwjclCV2n22VEWLccTzOAyJPs5MBLJA/tzJnV2BQ+7qEiJAmftOpCZ5OPHqacqU9tgj/f/LR7R9gfq5cy5iKGv5+D0pfIQIYA5DIiT0wnG2kp9M+RHCYwLxc3fxzi4Z6vJ3MtQlhBBCNMHh3LZCenz8nRQ+QgghRBPq5vhoqktRrEYfZyNaQwofIYQQogmqPhpHWBwAGhnu8mtS+AghhBAusMtwV0DweeFTUlLCvn37MBqb13XY0nZCCCFES9St5aOVtXz8WrMKnyNHjjBhwgQGDRpETk4Oqtr4dH1VVVm6dCljxoxhyJAhzJ07l6qqKufjy5cv55ZbbmH27NkMHz6cPXv2OB9btmwZmZmZpKen88gjj1BWVuZSuz179nDTTTcxZMgQVqxY0ZyXJ4QQQjRI1vIJDC4XPlarlaysLFJTU1m/fj0FBQVs2LCh0Ta5ubmsWrWKF154gTVr1rB//35mz54NQGFhIa+//jr5+fls2rSJyZMn8+qrrwKwd+9e8vLyWL16NRs3bsRisZCTk9Nku3PnzvH73/+e8ePHs27dOjZt2sTu3btb9MYIIYQQl5I7uwKDy4XPjh07MBqNzJgxgy5dujBt2jRyc3MbbZOXl8eDDz5Iv379SElJ4dFHH2Xbtm0A2O125s2bR/v27QHo3bs35eXlAOzfv59hw4aRkpJCcnIy48eP5/jx4022+9e//sVVV13Fww8/TNeuXfnDH/7QZI5CCCGEK+rW8pFtK/yby4XPoUOH6N+/P+Hh4QD06tWLgoKCRtuUlZWRmHhx1U6NRoNWW7uUeffu3Rk5ciQAJpOJ1atXM2bMGAB69OjBBx98wMmTJyktLSU3N5fMzMwm2x0+fJjrrrsORandJq9fv34cPHjQ1ZcohBBCNMg5ubmyCBx2H2cjWsrllZuNRiNJSUnOnxVFQaPRUF5eTkxMzBXb9OnTh61btzJ69GgANmzYwNChQ+s9Z/v27Tz22GMkJSWRlZUFwLBhw0hOTna2S0tL46GHHmqyndFo5JprrnE+x2AwUFJS4upLrEf56RbDrVQXz91xPRlbcvZObH/M2ZOxJWfvxJacmx9bNXRE1YSiOKxoTT/giE5q+MnNiNtS/hjbGzk3+Ty1qRnKFyxcuBCbzcaMGTOcx4YPH87bb79Nhw4drtimqKiIKVOmEBcXh9Fo5PDhw7z55ptkZGQ4n2Oz2fjyyy955plnGDx4MNnZ2WzZsoXXXnuN1157jfj4eHJycjAajSxatKjRdo899hjp6encf//9QO2wWL9+/fjmm29cezeEEEKIxiwaCKXfwaRN0G2Yr7MRLeByj09MTAxHjx6td8xkMhESEtJgm6SkJLZs2cKxY8dYuHAhCQkJ9YoeAJ1OR0ZGBk899RRZWVlkZ2eTn5/PxIkTSUlJAWDmzJkMHDiQiooKoqOjG2wXExPDuXPnXM6vMaWllbhWErpGUaBduyi3x/VkbMnZO7H9MWdPxpacvRNbcm5Z7OjIJEJLv6Py5LdYoga4LW5L+GNsb+TcFJcLn7S0tHoThYuKirBarQ0Oc11MRMFgMLBz507WrFnjPL5p0ybOnDnD5MmTAdBqtc75P3a7nbNnzzqfe/r0aefxxtqlpaWRn5/vbPftt9822BvVFFXF7b8UT8b1ZGzJ2Tux/TFnT8aWnL0TW3JuXuxL1/JpTg7++D57MrYnc26Ky5ObBw0aRGVlJXl5eQAsWbKEzMxMtFotRqORmpqaBtsuXryYcePGkZqa6jyWkpLCokWL+OCDDygqKmLRokWMHTsWgPT0dN5++23WrFnDxo0bmTZtGgMGDCAuLq7RdiNHjuSLL75g9+7d2Gw2li9fzvXXX9+S90UIIYS4TN0EZ9m2wn+53OOj0+mYN28e06dPZ8GCBdjtdlavXg3AbbfdRnZ2NqNGjbqs3YkTJ9i8eTObN2+udzw1NZU5c+Ywf/58KisrGTt2rHP+0KRJkzh9+jSLFy+mrKyMAQMG8OyzzzbZLj4+nieffJLf/e53GAwGIiIinO2EEEKI1pJFDP2fy5Ob65SUlHDgwAHS09OJj4/3VF6tcuLECQoKChg8eDAGg6FFMc6edf+4ZkJClNvjejK25Oyd2P6YsydjS87eiS05tyy2tvRb4teOxqGPpfR3X7stbkv4Y2xv5NwUl3t86nTo0KHF82a8JTk5meTkZF+nIYQQIsDYoy4sYmg5j2IpR9U3Ps9VtD0+36RUCCGE8BuhkTjCrwJAK/N8/JIUPkIIIUQzXNy6Qub5+CMpfIQQQohmuDjBWXp8/JEUPkIIIUQzOPfskh4fvySFjxBCCNEMzkUMpcfHL0nhI4QQQjSDQ9by8WtS+AghhBDNUNfjo6k8BfaGdy0QbZMUPkIIIUQzOCLao2r1KKodjbHY1+mIZpLCRwghhGgORSNbV/gxKXyEEEKIZrp0l3bhX6TwEUIIIZpJenz8lxQ+QgghRDPJnV3+SwofIYQQopnsMV0B0MhQl9+RwkcIIYRopnpDXarq42xEc0jhI4QQQjSTPbozABprJYrlvG+TEc0ihY8QQgjRXLpw7JEdANmzy99I4SOEEEK0gCNa9uzyR1L4CCGEEC1wcS0f6fHxJ1L4CCGEEC1QN8FZI7e0+xUpfIQQQogWuHhnlwx1+RMpfIQQQogWqFvLR4a6/IsUPkIIIUQLOIe6jMVgt/o4G+EqKXyEEEKIFlDDE1B1ESioaCuLfJ2OcJEUPkIIIURLKAr2mAu9PjLc5Tek8BFCCCFayC5r+fgdKXyEEEKIFnLe2SU9Pn5DCh8hhBCihZyLGMpaPn5DCh8hhBCihRyylo/f0fk6ASGEEMJfOef4lJ8AVQVF8XFG7qExFkNlCRqlA/bIRF+n41ZS+AghhBAtZI9OQkVBsVWhVJeiRiT4OqVWCzu4BsPHT4LqIE7RYByRg7nvRF+n5TYy1CWEEEK0lFaPw9Cp9v8GwDwfjbEYw8dPoqgOABTVgeHjv9T2AAUIKXyEEEKIVgikXdq15wudRU8dRbWjO7XbRxm5nxQ+QgghRCsE1GalVuMVD8dsfZSYvHvQH8kDm9m7ObmZz+f4lJSUcOrUKXr27InBYPB1OkIIIUSzOAJoEcOIr1cC1M5bQkVFgz2+O9pzRwk99Smhpz7FoY/F3GsC5r4Tsbfr7eOMm69ZPT5HjhxhwoQJDBo0iJycHFRVbfT5qqqydOlSxowZw5AhQ5g7dy5VVVXOx5cvX84tt9zC7NmzGT58OHv27HE+tmzZMjIzM0lPT+eRRx6hrKzM+di6deu4/vrrSU1NZfLkyZw+fdqldkIIIYS71Q11+fu2FSHFnxH6/Q5UjY7zEzbCpM2UPbCbsokfcu63uzBlPIbd0AmN5TwR+18nfu0oYnNvI+zgGrCafJ2+y1wufKxWK1lZWaSmprJ+/XoKCgrYsGFDo21yc3NZtWoVL7zwAmvWrGH//v3Mnj0bgMLCQl5//XXy8/PZtGkTkydP5tVXXwVg79695OXlsXr1ajZu3IjFYiEnJweAzz//nFdeeYUFCxawbdu2eo811k4IIYTwhItDXf5d+ETseQEAc597sXfKgG434DDU3sruiE6iasifOffb3ZTf8g8sKeNQNTpCSvYR9dHjtHsjHcNHT6Ar+U/tbf1tmMuFz44dOzAajcyYMYMuXbowbdo0cnNzG22Tl5fHgw8+SL9+/UhJSeHRRx9l27ZtANjtdubNm0f79u0B6N27N+Xl5QDs37+fYcOGkZKSQnJyMuPHj+f48eNAbcE0Z84cMjMz6dixI3feeSdff/11k+2EEEIIT3Cu5WP60W/nv4QUfUroqV2omlCqBj7a8BM1WqzJI6m4aRml9+/B+PNsbDHd0NSYCD/4FnG5txK3bgxh+5ejmM97Lf/mcHmOz6FDh+jfvz/h4eEA9OrVi4KCgkbblJWVkZh4ceEjjUaDVqsFoHv37nTv3h0Ak8nE6tWrGTNmDAA9evRg7dq13HvvvURGRpKbm0tmZiYAd999d71zFBYWkpyc3GS75nL3GlSnjRaOlFuI1UJ7g94vYkvO3ontjzl7Mrbk7J3YkrMbY4fH4QiNQmOtRFf5Pfb4Hm0/50upKpF1vT2p96FGJ7oW29Ae88A/YE7/Pbri3YR98xb6gi3oSr8l6pNZGHY+i+WamzGn3oct8TpQFM6VHOfHw8cJje1KfIeuLc/5Clz93na58DEajSQlJV1yAgWNRkN5eTkxMTFXbNOnTx+2bt3K6NGjAdiwYQNDhw6t95zt27fz2GOPkZSURFZWFgDDhg0jOTnZ2S4tLY2HHnrosvhlZWWsW7eOhQsXNqudK9q1i2pRuytZt/ckMzYcwKGCRoG5t6UyYWBS0w1dsP6LImb/6xu3x/ZUXE/Glpz9P7bk7J3YkrMHYsd1hZIDhNt+xBHdzz9yvkBz7ENCftiLqguDEX/m3SOlzY8dMxJHn5FUV59H9/Xb6L5chebMQcKObCTsyEYc8dfwva4r15R8iFZRsasKX/Sbw+AJj7Uo59ZQ1KZmKF+wcOFCbDYbM2bMcB4bPnw4b7/9Nh06dLhim6KiIqZMmUJcXBxGo5HDhw/z5ptvkpGR4XyOzWbjyy+/5JlnnmHw4MFkZ2ezZcsWXnvtNV577TXi4+PJycnBaDSyaNGievEfe+wxqqqqWLJkCYDL7VxRWlrplmHKkkoLty75DEfbHvIUQgjRCn8LeYmbtHuZU3M/b9jH+TqdZlDJC53FtZoCltlu4hnbb90Wt79SwD3aj7hNuwuDcvkQoE3VUHD3Dtp17OqWMyqKa50WLs/xiYmJ4dy5c/WOmUwmQkJCGmyTlJTEli1bmDdvHomJiQwdOrRe0QOg0+nIyMjgqaeeck6Wzs/PZ+LEiaSkpBAbG8vMmTN5//33qaiocLbLzc3l888/57nnnnMec6Wdq1TVPf+dPFctRY8QQgS4E2ptB0AX5XQTz2xbRmr+w7WaAqpUPf9ru82NkRW+UruTbZvCYMti/m4bf9kzdIqD8uIjbvu+dbWzwuWhrrS0tHqTmYuKirBarQ0OczlfuqJgMBjYuXMna9ascR7ftGkTZ86cYfLkyQBotVrn/B+73c7Zs2edz627Xd1utwO1k5ife+45/vd//5eEhIv7ojTVzhc6x4WjUahX/GgUePuBDNpHtW6893SlhV+98bnbY3sqridjS87+H1ty9k5sydkzsQ0HC+Hfm7k3xcaocUP9ImdUlY4bnoVSsF37X2wYcrPH8i4r6Y49bwta5WJgm6ohulPPFsdsKZcLn0GDBlFZWUleXh633347S5YsITMzE61Wi9FoRK/XN9j7s3jxYsaNG0dqaqrzWEpKCrNmzaJz58706dOHRYsWMXbsWADS09NZsWIFHTt2JCwsjJUrVzJgwADi4uI4e/YsWVlZTJkyhdTUVEym2rUDIiMjG23nKx2i9GSP7sFzHxx1jpdmj+5BcnxEq2Mnx0d4JLan4krO/p+zJ2NLzpKzP+esiesKQGjlScJDtH6Rc+ixdwkt/QZHSCTWgX/waN7hSSl82nMmmUeeQ6c4sKkadvbMpo+bhrmaw+U5PgBbt25l+vTpREZGYrfbWb16NT169GDkyJFkZ2czatSoy9qcOHGCO++8k82bN9OpU6d6j73zzju88sorVFZWMnbsWGbOnEl4eDgWi4WFCxfy3nvvUVZWxoABA3j22Wfp0qULb7zxBvPnz7/sPIcPH260XXOdPeueOT51ThstVDgUojWqR2b2eyK25Oyd2P6YsydjS87eiS05uze2pvw47VZfj6rVc/a/v3PeYtRmc1YdxK0bg670EKaBj1J13RPui92IcyXHsZWfRBfTxSN3dSUkND3Hp1mFD9RuMXHgwAHS09OJj49vcYJtnbsLn7pfiLvjejK25Oyd2P6YsydjS87eiS05uzm2vYaEv1+DojoofeALHJEd2nTO+qObiH7/9zhCozj3252oYfVHRvz5d9iUZu/V1aFDhwbv4hJCCCGCkjYEh+FqtJXfoyk/4Sx82iSHnYi9LwJQ3X/KZUVPoJPd2YUQQgg3qNuzq61vVqo/+g66sqM49DFU9/+dr9PxOil8hBBCCDfwiz27HDYi9r4EQPW1Waj6aB8n5H1S+AghhBBu4OzxacO7tOsPb0BXXogjLJ7qfv/l63R8QgofIYQQwg2cm5W21aEuew2Rn78MQNWA36OGGnybj49I4SOEEEK4gePCUJemjRY+YYfeRltxEkf4VVSnPeDrdHxGCh8hhBDCDZxDXVWnoabKx9n8hN1CxOevAFA18GEICfdxQr4jhY8QQgjhBqo+Boe+dhuntjbcFXZwLVpjMfbIDlSn/trX6fiUFD5CCCGEm7TJeT62aiK+eBWAqoGPgC54e3tACh8hhBDCbdrinV3h37yJ1lSC3ZCIue9EX6fjc1L4CCGEEG7iaGtr+dRUE/HF/wOgKuNR0Lp3vzB/JIWPEEII4SZ1ixhq2kiPT/iBN9BUn8Ee3QVz73t8nU6bIIWPEEII4Sb2mK5A25jjo1iNRPxnMQCmjD+BNsTHGbUNUvgIIYQQbnJx24rvQXX4NJfw/SvQmMuwxXTF0muCT3NpS6TwEUIIIdzEYeiEqtGhOKxojD/6LA/FUkH4l/8LQNWgqaDR+SyXtkYKHyGEEMJdNDrsUUmAbyc4h3+1DI2lHFtcdyw9bvdZHm2RFD5CCCGEGzkurOXjq60rFPN5wr9aCkDVoGmg0fokj7ZKCh8hhBDCjZzzfHx0Z1f4l0vQWCuxxffC0v0Wn+TQlknhI4QQQriRcxFDHwx1KdXnCN//OgCmwdNBka/5n5J3RAghhHCji3d2eX+oK+I/f0NTY6Im4WdYU27y+vn9gRQ+QgghhBs51/Lx8lCXUnWG8ANvAFA1eDooilfP7y+k8BFCCCHcyBHdGQCN+RyKtdJr543YtxjFVk1N+2uxdh3ltfP6Gyl8hBBCCDdSQ6NwhMUDoKn43ivn1Jh+JPzrfwBgGvJn6e1phBQ+QgghhJtd3KX9uFfOF/HFayh2CzUdM6jpPNwr5/RXUvgIIYQQbnbxlnbPT3DWVJ4i7Ju3ADANeVx6e5oghY8QQgjhZnYvLmIY/vkiFIcVa+J11Fyd6fHz+TspfIQQQgg3uzjU5eE7u8qOE/btWgCqZG6PS6TwEUIIIdzM4VzLx8OFz46FKA4b1qQbqEm8zrPnChBS+AghhBBu5hzqqiwCu80j59Ce+gz+Uze3588eOUcgksJHCCGEcDOHoSOqJhTFYYOKU26PH/bNm8RunAA4UAHduSNuP0egksJHCCGEcDdFg/3CQoaUHXdbWE35cQzbn8Lw8ZPUzeZRAMPHf0FjLHbbeQKZztcJCCGEEIHIHt0F3fkCKCuE6PSWB7KZ0R/7P8IOriH01KdXfIqi2tGWH8dhSGz5eYKEFD5CCCGEBzgu3NlF2XFIbn57bekhwg6uIezwejSW8wCoKNQkDiGk+DMUVOdzVUXr3CNMNM7nhU9JSQmnTp2iZ8+eGAwGX6cjhBBCuEXdBOdmDXVZTYR99y/CDq4hpGTfxViGTph734O5z704opMIO7gGw8d/QVHtqIoW44jnpbfHRc0qfI4cOcKMGTM4efIkd911F0888QRKI2sGqKrKsmXL+Oc//0l5eTk333wzjz/+OBEREQAsX76cv/3tb3Ts2JHi4mL+9re/MXjwYACWLVvG8uXLMZvNDB06lL/+9a/ExcUBsG7dOhYtWkRZWRlDhgzh+eefp3379vXOPXXqVOLj43n66aeb9YYIIYQQ7lC3lg/nCht/oqqiO/0VYQffQn/0HTQ1ptrDGh3WrqMw970Pa+fhoNE6m5j7TqQmeTjx6mnKlPbYI6XocZXLk5utVitZWVmkpqayfv16CgoK2LBhQ6NtcnNzWbVqFS+88AJr1qxh//79zJ49G4DCwkJef/118vPz2bRpE5MnT+bVV18FYO/eveTl5bF69Wo2btyIxWIhJycHgM8//5xXXnmFBQsWsG3btnqP1fnkk0/YvXs3f/rTn5r1ZgghhBDuUrdtRUM9Por5PGH7VxC3bgxxubcQfvAtNDUmbDFdMf58BqX376HipmVYk0fWK3rqOAyJ0O0G6elpJpcLnx07dmA0GpkxYwZdunRh2rRp5ObmNtomLy+PBx98kH79+pGSksKjjz7Ktm3bALDb7cybN8/ZU9O7d2/Ky8sB2L9/P8OGDSMlJYXk5GTGjx/P8ePHgdqCac6cOWRmZtKxY0fuvPNOvv76a+c5zWYzc+fOZfr06URHRzfrzRBCCCHcxVn4mM+jLT1U+/9VlZBTu4j64BHavTGQqE+eRlf6LapWj7nnHZy//W3Kfv0J1ekPo0a2bzi4aDGXh7oOHTpE//79CQ8PB6BXr14UFBQ02qasrIzExIuVqEajQautrVq7d+9O9+7dATCZTKxevZoxY8YA0KNHD9auXcu9995LZGQkubm5ZGbW7j9y99131ztHYWEhyckXZ40tXrwYs9mMTqdj165dXHfddY0OxzXE3at+18XzxGrinootOXsntj/m7MnYkrN3YkvOno+t/+4dVGpvN49dMxpryk3oSr9FW35x6MvWrjfmvvdh6XUnalhsbQ4+zNnTsb2Rc1NcLnyMRiNJSUmXnEBBo9FQXl5OTEzMFdv06dOHrVu3Mnr0aAA2bNjA0KFD6z1n+/btPPbYYyQlJZGVlQXAsGHDSE5OdrZLS0vjoYceuix+WVkZ69atY+HChQAUFxezYsUK+vXrR3FxMf/4xz/o1KkTr732WrOLn3btopr1fF/H9WRsydk7sf0xZ0/Glpy9E1ty9lDs8lPw0ZPOHxVU9Me21P4QaoCfTYD0SeiuTsegKLTm1h6/eD+8FNcVLhc+Wq2W0NDQesf0ej1ms7nBwmfq1KlMmTKF++67D6PRyOHDh3nzzTfrPWfo0KEsXbqUZ555hoULF5Kdnc2WLVsoLi7m3XffJT4+npycHB5//HEWLVpUr+3cuXMZMGAAI0aMAGoLq4SEBFasWEFoaCj3338/I0eO5NNPP+X666939aUCUFpaiao2/TxXKUrtL9rdcT0ZW3L2Tmx/zNmTsSVn78SWnD0bO6ToADGq47LjVQOyqBo0FUIjaw+UGlt8Dn96Pzwd99LYTXG58ImJieHo0aP1jplMJkJCQhpsk5SUxJYtWzh27BgLFy4kISGBjIyM+gnodGRkZPDUU0+RlZVFdnY2+fn5TJw4kZSUFABmzpzJwIEDqaiocM7byc3N5fPPPycvL88Zq6SkhOuuu85ZoBkMBpKTkykqKnL1ZTqpKm7/pXgyridjS87eie2POXsytuTsndiSs2di22K6oSoalEuKH1XRUt1vMmpIJLgxf394P7wV1xUuT25OS0vjq6++cv5cVFSE1WptsLenjqIoGAwGdu7cyfTp053HN23axPLly50/a7Va5/wfu93O2bNnnY+dPn3aeRxqJz8/99xzvPjiiyQkJDif17FjRywWi/Nnh8PBjz/+WG+ekRBCCOFpDkMixhE5qErt95qstdN2uNzjM2jQICorK8nLy+P2229nyZIlZGZmotVqMRqN6PX6Bnt/Fi9ezLhx40hNTXUeS0lJYdasWXTu3Jk+ffqwaNEixo4dC0B6ejorVqygY8eOhIWFsXLlSgYMGEBcXBxnz54lKyuLKVOmkJqaislUu95BZGQkN910ExMmTOC9996jf//+rFq1CqvVSnp6K5YKF0IIIVpA1tppm1wufHQ6HfPmzWP69OksWLAAu93O6tWrAbjtttvIzs5m1KhRl7U7ceIEmzdvZvPmzfWOp6amMmfOHObPn09lZSVjx45lxowZAEyaNInTp0+zePFiysrKGDBgAM8++ywAmzdvprS0lJdffpmXX37ZGe/w4cOkpKTw0ksv8fLLL3Ps2DG6dOnC4sWLZUVoIYQQPuEwJEJCLxxnK906vCVaTlHV5o2ylZSUcODAAdLT04mPj/dUXj539qz7J3QlJES5Pa4nY0vO3ontjzl7Mrbk7J3YkrN3Yvtjzp6M7Y2cm9Lsvbo6dOhAhw4dWpSUEEIIIYQvuTy5WQghhBDC30nhI4QQQoigIYWPEEIIIYKGFD5CCCGECBpS+AghhBAiaEjhI4QQQoigIYWPEEIIIYKGFD5CCCGECBpS+AghhBAiaDR75eZgoSieiefuuJ6MLTl7J7Y/5uzJ2JKzd2JLzt6J7Y85ezK2N3Ju8nnN3atLCCGEEMJfyVCXEEIIIYKGFD5CCCGECBpS+AghhBAiaEjhI4QQQoigIYWPEEIIIYKGFD5CCCGECBpS+AghhBAiaEjhI4QQQoigIYVPACgrK2Pfvn2cO3fO16kIEdTkWhSi7ZPCxwu2bt3KL37xC/r27cvdd99NQUGB22Ln5+czZswY/vrXv3LjjTeSn5/vtth1HnzwQTZs2OCWWPPmzaNXr17O/0aPHu2WuJd64YUXyMrKclu8DRs21Mu57j93vCd5eXmMGDGCAQMG8MADD1BUVOSGjGutX7+eW265hYyMDKZNm9Zmv4zLysoYOXLkZa+9oeOtje2O6/FKcd11LTb1ult6PV4prruux8Zybu31+NPY7roer5Szu67HK8Vuy9djY9dEa67DhuJ68jvRJarwqBMnTqiDBg1S8/Pz1TNnzqiPPvqoes8997gldnl5uTpkyBD18OHDqqqq6saNG9URI0a4JXadd955R+3Zs6e6fv16t8S755571I8//lgtLy9Xy8vL1crKSrfErXP48GF1wIAB6okTJ9wW02KxOPMtLy9Xf/jhB3XIkCHqyZMnWxX3xIkT6vDhw9Wvv/5aPXXqlDpjxgz1N7/5jVty/vTTT9Vrr71W/fe//60WFRWpU6ZMUSdOnNiqmOfOnVNvvPFG9fvvv3ceO3z4sHrnnXeqGRkZ6vPPP686HI5mxSwtLVV/9atfqT179qwXt6HjrY3tjuvxSnHddS029bpbej02FNcd12NjObf2erxSbHdcjw19NtxxPV4ptjuuxw8++EAdOXKk2qdPH/Wuu+5Sv/vuO1VVW38NNnZNtOY6bCiuJ78TXSU9Ph5WUFDA1KlTufnmm0lISGDixIl8/fXXboltMpnIzs6mZ8+eAPTu3Zvy8nK3xAY4f/48OTk5dOvWzS3xbDYbR44cISMjg+joaKKjozEYDG6JDaCqKrNmzWLSpEl06dLFbXFDQ0Od+UZHR5OXl8eYMWPo3Llzq+IePHiQ/v37k5qaSmJiInfeeSeFhYVuyTkvL4+7776boUOHcvXVV/PEE0/wxRdfUFZW1qJ4586dIysri1OnTjmPWa1WsrKySE1NZf369RQUFDT7r+5p06Zx8803u3y8tbHdcT1eKa67rsXGXndrrscrxXXX9dhQzu64Hq8U2x3X45Xiuut6vFLs1l6PJ0+eJDs7m+nTp7Njxw4SExOZOXOmW67Bxq6J1lyHDcX15Heiy7xaZgn1rbfeUm+++Wa3x7Varerjjz+uPvnkk26L+Ze//EWdNWuW+uSTT7qlx+frr79Wr732WnXUqFFqWlqaOnnyZPXUqVNuyLTW2rVr1f79+6u5ubnqhx9+qFqtVrfFrmM2m9Wf//znLe6FuNTRo0fVwYMHq998841aUVGhTp06VX3iiSfckKWq/u53v1NXrFjh/LmgoEDt2bOnWlFR0aJ4kyZNUt944416f/l98MEH6qBBg9SqqipVVVX122+/Ve+9995mxa37K/2nf1E2dNwdsS/VkuuxqbituRYbi92a6/FKcd11PTaUszuux6be65Zej1eK667r8UqxW3s9fvjhh+pbb73l/HnXrl1qamqqW67Bn7r0mnDHdXiluK4c9yTp8fEiq9XK8uXLue+++9wa99ChQwwdOpRPP/2U7Oxst8TcvXs3u3bt4vHHH3dLPKj9C6BHjx78z//8D/n5+YSEhDBr1iy3xDaZTLz88sskJyfz448/smLFCn7zm99gsVjcEr/Opk2b6N+/P0lJSa2O1b17d8aOHcsdd9xBRkYGX331FU8++aQbsoQ+ffqwbds2VFUFaudF9OvXj6ioqBbFmzdvHpMmTap37NChQ/Tv35/w8HAAevXq1eyx+ob+Sm9tb5orMVp6PTYWt7XXYkOxW3s9Ximuu67HK8V21/XY1O+wpdfjleK663q8UuzWXo833ngjEydOdP5cWFhIcnKyW67BS/30mnDHdXiluE0d9zQpfLzo5ZdfJiIigl/96ldujdurVy/eeOMNunfvzowZM1odz2KxMHv2bObMmePWoajbbruNt99+m379+tG5c2eefvppPv30U4xGY6tjf/DBB1RXV7Ny5Uoefvhhli9fTkVFBXl5ea1P/BJr167l3nvvdUusL7/8ko8++oh//vOf7Nu3j1tuuYUpU6Y4/3FsjcmTJ1NTU8Odd97Jvffey9KlS/n1r3/d4nhX+gfQaDTW+8JRFAWNRuPW4VZP8sT16O5rEeR6bEwwXo+XFgvuvgY99R3VUFxPna8pUvh4yaeffsratWv5n//5H0JCQtwaW1EU+vbty/PPP8+2bdta/cWzePFifvaznzFixAj3JNiA6OhoHA4Hp0+fbnWsH3/8kX79+hEbGwuATqejV69ebr1L6sSJE5w8eZLMzEy3xNuyZQvjx4+nX79+REZG8thjj1FUVMShQ4daHTs2Npa1a9fy8ssv07NnT1JSUrj11lvdkPVFWq2W0NDQesf0ej1ms9mt5/EET12P7r4WQa7HhgTr9XhpseDOa9BT10RDcT35ndgUnVfPFqS+//57/vznPzNnzhy6d+/utri7du1ix44dzu5YrVYLgEbTunp206ZNlJWVkZGRAYDZbObdd99l//79zJkzp8Vx58+fT//+/Z2T5Q4cOIBGo6FTp06tyhegY8eOl3WjFxcXM2TIkFbHrvPuu+8yYsQIt12kdru93i2tJpOJqqoq7Ha7W+IDtG/fng8++IB58+Y5Px/uEhMTw9GjR+sdM5lMXv9HrLk8cT166loEuR4bEozXY12x8PbbbxMSEuK2a9BT31ENxfXU+VwlhY+Hmc1m/vu//5tRo0bxi1/8ApPJBEBERASKorQqdkpKCg8//DBdu3Zl2LBhvPzyywwdOrTF8zjqvPXWW9hsNufPCxYsoH///txxxx2titunTx9efvllrrrqKmw2G/PmzeOOO+5wjk+3xogRI3jmmWdYs2YNN954I++//z7ffvstL774Yqtj1/nkk0+488473RYvPT2d7Oxs3njjDdq1a8c///lPEhIS6NWrl9vOsWrVKlJSUhg1apTbYtZJS0sjNzfX+XNRURFWq5WYmBi3n8tdPHU9eupaBLkeGxJs1+OVigV3XIOeuiYaiqvVaj32negqKXw87N///jcFBQUUFBTw9ttvO49v27at1RNkO3TowCuvvML8+fPJycnh+uuvZ+HCha1NmY4dO9b7OSIigri4OOLj41sV9/bbb6egoIA//OEPREZGMmrUKKZNm9aqmHViY2NZtmwZzz//PM8//zwJCQm89NJLbpmEDLUX8VdffcVf//pXt8QDuPnmmyksLGTlypWcOXOGHj16sGjRIrf9BVtRUcHrr7/O0qVL3RLvpwYNGkRlZSV5eXncfvvtLFmyhMzMTLf3LLmTp65HT12LINfjlQTb9dhQEZGRkdHqa9BT10RDcWfMmOGx70RXKao7Zm4JIYJCr1696v0DtXXrVqZPn05kZCR2u53Vq1fTo0cPH2cpRGDZunUrDz/88GXHt23bxqFDh+QabCYpfIQQrVJSUsKBAwdIT09vdS+EEKL55BpsHil8hBBCCBE05HZ2IYQQQgQNKXyEEEIIETSk8BFCCCFE0JDCRwghhBBBQwofIYQQQgQNKXyEEEIIETSk8BFCCCFE0JDCRwghhBBBQwofIYQQQgSN/w/rEWfadzMy4AAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 111
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:13:16.215746Z",
     "start_time": "2024-09-19T12:13:16.211107Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def select_fin(i,train_1,target_1):\n",
    "    sel = SelectKBest(mutual_info_classif, k=i)\n",
    "    sel = sel.fit(train_1, target_1)\n",
    "    train_sel = sel.transform(train_1)\n",
    "    test_sel = sel.transform(test_data_1)\n",
    "    return train_sel,test_sel"
   ],
   "id": "a244218cf0dd93e4",
   "outputs": [],
   "execution_count": 112
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:13:48.261524Z",
     "start_time": "2024-09-19T12:13:24.664718Z"
    }
   },
   "cell_type": "code",
   "source": "train_sel,test_sel=select_fin(4,train_data_1,target)",
   "id": "264341801e5584ee",
   "outputs": [],
   "execution_count": 113
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:14:15.308232Z",
     "start_time": "2024-09-19T12:14:14.961722Z"
    }
   },
   "cell_type": "code",
   "source": "fpr_5,tpr_5,clf_5,auc_test_5=model(train_sel,target)#k=2 0.6419733",
   "id": "314b9c68d6cf3c8a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5931798111646214\n",
      "Test AUC Score 0.5557045844514406\n"
     ]
    }
   ],
   "execution_count": 115
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:14:33.562314Z",
     "start_time": "2024-09-19T12:14:33.205515Z"
    }
   },
   "cell_type": "code",
   "source": "fpr_6,tpr_6,clf_6,auc_test_6=model(train_sel,target)#k=3 0.6365505",
   "id": "4ab5a953243169db",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5931798111646214\n",
      "Test AUC Score 0.5557045844514406\n"
     ]
    }
   ],
   "execution_count": 116
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:14:49.028540Z",
     "start_time": "2024-09-19T12:14:48.672773Z"
    }
   },
   "cell_type": "code",
   "source": "fpr_7,tpr_7,clf_7,auc_test_7=model(train_sel,target)#k=4 0.6323718",
   "id": "eca66e1f731d032d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5931798111646214\n",
      "Test AUC Score 0.5557045844514406\n"
     ]
    }
   ],
   "execution_count": 117
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:14:59.308644Z",
     "start_time": "2024-09-19T12:14:59.296621Z"
    }
   },
   "cell_type": "code",
   "source": "a_score,b_score=select(X_smote,Y_smote)",
   "id": "358cebc4545cd95f",
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_smote' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_20168\\1962973455.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0ma_score\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mb_score\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mselect\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX_smote\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mY_smote\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m: name 'X_smote' is not defined"
     ]
    }
   ],
   "execution_count": 118
  }
 ],
 "metadata": {
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